


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 				A       :
					'''simple docstring'''






					def __init__(self       :		List[Any] ,   _UpperCAmelCase       :		Tuple ,   _UpperCAmelCase       :		List[str]=13 ,   _UpperCAmelCase       :		Tuple=7 ,   _UpperCAmelCase       :		Dict=True ,   _UpperCAmelCase       :		List[str]=True ,   _UpperCAmelCase       :		Any=True ,   _UpperCAmelCase       :		str=True ,   _UpperCAmelCase       :		Optional[int]=99 ,   _UpperCAmelCase       :		Tuple=32 ,   _UpperCAmelCase       :		Optional[Any]=2 ,   _UpperCAmelCase       :		Union[str, Any]=4 ,   _UpperCAmelCase       :		Optional[int]=37 ,   _UpperCAmelCase       :		Any="gelu" ,   _UpperCAmelCase       :		List[Any]=0.1 ,   _UpperCAmelCase       :		List[str]=0.1 ,   _UpperCAmelCase       :		List[Any]=512 ,   _UpperCAmelCase       :		List[str]=16 ,   _UpperCAmelCase       :		Tuple=2 ,   _UpperCAmelCase       :		Optional[int]=0.02 ,   _UpperCAmelCase       :		Optional[int]=False ,   _UpperCAmelCase       :		Optional[int]=True ,   _UpperCAmelCase       :		Optional[Any]="None" ,   _UpperCAmelCase       :		Dict=3 ,   _UpperCAmelCase       :		Optional[int]=4 ,   _UpperCAmelCase       :		Any=None ,   )		->  Optional[int]:




							"""simple docstring"""


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

					def     lowerCamelCase__	(self       :		Optional[Any]						)		->  int:




							"""simple docstring"""


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

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

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

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

							lowercase__	     =						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     lowerCamelCase__	(self       :		List[str] ,   _UpperCAmelCase       :		Optional[Any] ,   _UpperCAmelCase       :		Any ,   _UpperCAmelCase       :		Dict ,   _UpperCAmelCase       :		Any ,   _UpperCAmelCase       :		Any ,   _UpperCAmelCase       :		Dict ,   _UpperCAmelCase       :		List[str]						)		->  Optional[Any]:




							"""simple docstring"""


							lowercase__	     =						TFDebertaVaModel(config=_UpperCAmelCase						)
							lowercase__	     =						{"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}

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

							lowercase__	     =						model(_UpperCAmelCase						)

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

					def     lowerCamelCase__	(self       :		Any ,   _UpperCAmelCase       :		Optional[Any] ,   _UpperCAmelCase       :		List[Any] ,   _UpperCAmelCase       :		List[str] ,   _UpperCAmelCase       :		List[str] ,   _UpperCAmelCase       :		Optional[Any] ,   _UpperCAmelCase       :		str ,   _UpperCAmelCase       :		Tuple						)		->  List[str]:




							"""simple docstring"""


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

					def     lowerCamelCase__	(self       :		List[Any] ,   _UpperCAmelCase       :		Tuple ,   _UpperCAmelCase       :		Union[str, Any] ,   _UpperCAmelCase       :		int ,   _UpperCAmelCase       :		Union[str, Any] ,   _UpperCAmelCase       :		List[str] ,   _UpperCAmelCase       :		Union[str, Any] ,   _UpperCAmelCase       :		Any						)		->  Union[str, Any]:




							"""simple docstring"""


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

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

					def     lowerCamelCase__	(self       :		List[Any] ,   _UpperCAmelCase       :		Tuple ,   _UpperCAmelCase       :		List[str] ,   _UpperCAmelCase       :		Any ,   _UpperCAmelCase       :		Union[str, Any] ,   _UpperCAmelCase       :		Union[str, Any] ,   _UpperCAmelCase       :		List[str] ,   _UpperCAmelCase       :		Any						)		->  str:




							"""simple docstring"""


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

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




							"""simple docstring"""


							lowercase__	     =						TFDebertaVaForQuestionAnswering(config=_UpperCAmelCase						)
							lowercase__	     =						{
							    """input_ids""": input_ids,
							    """attention_mask""": input_mask,
							    """token_type_ids""": token_type_ids,
							}

							lowercase__	     =						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     lowerCamelCase__	(self       :		Any						)		->  Any:




							"""simple docstring"""


							lowercase__	     =						self.prepare_config_and_inputs()
							(
							    (
							    lowercase__
							)     ,      (
							    lowercase__
							)     ,      (
							    lowercase__
							)     ,      (
							    lowercase__
							)     ,      (
							    lowercase__
							)     ,      (
							    lowercase__
							)     ,      (
							    lowercase__
							)     ,      
							)	     =						config_and_inputs
							lowercase__	     =						{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
							return config, inputs_dict






@require_tf
class 				A       (			UpperCAmelCase__					,    UpperCAmelCase__					,    unittest.TestCase				):
					'''simple docstring'''






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

					A__     =					False
					A__     =					False

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




							"""simple docstring"""


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

					def     lowerCamelCase__	(self       :		List[str]						)		->  List[str]:




							"""simple docstring"""


							self.config_tester.run_common_tests()

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




							"""simple docstring"""


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

					def     lowerCamelCase__	(self       :		Any						)		->  Any:




							"""simple docstring"""


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

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




							"""simple docstring"""


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

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




							"""simple docstring"""


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

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




							"""simple docstring"""


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


					@slow
					def     lowerCamelCase__	(self       :		Any						)		->  int:




							"""simple docstring"""


							lowercase__	     =						TFDebertaVaModel.from_pretrained("""kamalkraj/deberta-v2-xlarge"""						)
							self.assertIsNotNone(_UpperCAmelCase						)






@require_tf
class 				A       (			unittest.TestCase				):
					'''simple docstring'''






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




							"""simple docstring"""


							pass


					@slow
					def     lowerCamelCase__	(self       :		Dict						)		->  str:




							"""simple docstring"""


							lowercase__	     =						TFDebertaVaModel.from_pretrained("""kamalkraj/deberta-v2-xlarge"""						)
							lowercase__	     =						tf.constant([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]						)
							lowercase__	     =						tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]						)
							lowercase__	     =						model(_UpperCAmelCase ,   attention_mask=_UpperCAmelCase						)[0]

							lowercase__	     =						tf.constant(
							    [[[0.2_356, 0.1_948, 0.0_369], [-0.1_063, 0.3_586, -0.5_152], [-0.6_399, -0.0_259, -0.2_525]]]						)
							tf.debugging.assert_near(output[:, 1:4, 1:4] ,   _UpperCAmelCase ,   atol=1E-4						)





import os
import tempfile
import unittest

from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import (
	    FlaubertForMultipleChoice,
	    FlaubertForQuestionAnswering,
	    FlaubertForQuestionAnsweringSimple,
	    FlaubertForSequenceClassification,
	    FlaubertForTokenClassification,
	    FlaubertModel,
	    FlaubertWithLMHeadModel,
	)
	from transformers.models.flaubert.modeling_flaubert import FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST



class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					def __init__(self       :		Optional[Any] ,   _UpperCAmelCase       :		Optional[Any] ,   _UpperCAmelCase       :		Any=13 ,   _UpperCAmelCase       :		str=7 ,   _UpperCAmelCase       :		str=True ,   _UpperCAmelCase       :		List[Any]=True ,   _UpperCAmelCase       :		Optional[int]=True ,   _UpperCAmelCase       :		List[Any]=True ,   _UpperCAmelCase       :		Tuple=True ,   _UpperCAmelCase       :		Optional[int]=False ,   _UpperCAmelCase       :		Any=False ,   _UpperCAmelCase       :		List[Any]=False ,   _UpperCAmelCase       :		Tuple=2 ,   _UpperCAmelCase       :		Optional[int]=99 ,   _UpperCAmelCase       :		Any=0 ,   _UpperCAmelCase       :		Dict=32 ,   _UpperCAmelCase       :		Tuple=5 ,   _UpperCAmelCase       :		Union[str, Any]=4 ,   _UpperCAmelCase       :		Union[str, Any]=0.1 ,   _UpperCAmelCase       :		Optional[Any]=0.1 ,   _UpperCAmelCase       :		List[str]=512 ,   _UpperCAmelCase       :		Tuple=12 ,   _UpperCAmelCase       :		int=2 ,   _UpperCAmelCase       :		Tuple=0.02 ,   _UpperCAmelCase       :		Union[str, Any]=3 ,   _UpperCAmelCase       :		Optional[Any]=4 ,   _UpperCAmelCase       :		Dict="last" ,   _UpperCAmelCase       :		Any=None ,   _UpperCAmelCase       :		int=None ,   )		->  int:




							"""simple docstring"""


							lowercase__	     =						parent
							lowercase__	     =						batch_size
							lowercase__	     =						seq_length
							lowercase__	     =						is_training
							lowercase__	     =						use_input_lengths
							lowercase__	     =						use_token_type_ids
							lowercase__	     =						use_labels
							lowercase__	     =						gelu_activation
							lowercase__	     =						sinusoidal_embeddings
							lowercase__	     =						causal
							lowercase__	     =						asm
							lowercase__	     =						n_langs
							lowercase__	     =						vocab_size
							lowercase__	     =						n_special
							lowercase__	     =						hidden_size
							lowercase__	     =						num_hidden_layers
							lowercase__	     =						num_attention_heads
							lowercase__	     =						hidden_dropout_prob
							lowercase__	     =						attention_probs_dropout_prob
							lowercase__	     =						max_position_embeddings
							lowercase__	     =						type_vocab_size
							lowercase__	     =						type_sequence_label_size
							lowercase__	     =						initializer_range
							lowercase__	     =						num_labels
							lowercase__	     =						num_choices
							lowercase__	     =						summary_type
							lowercase__	     =						use_proj
							lowercase__	     =						scope

					def     lowerCamelCase__	(self       :		List[str]						)		->  List[str]:




							"""simple docstring"""


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

							lowercase__	     =						None
							if self.use_input_lengths:
									lowercase__	     =						(
									    ids_tensor([self.batch_size] ,   vocab_size=2						) + self.seq_length - 2
									)  # small variation of seq_length

							lowercase__	     =						None
							if self.use_token_type_ids:
									lowercase__	     =						ids_tensor([self.batch_size, self.seq_length] ,   self.n_langs						)

							lowercase__	     =						None
							lowercase__	     =						None
							lowercase__	     =						None
							if self.use_labels:
									lowercase__	     =						ids_tensor([self.batch_size] ,   self.type_sequence_label_size						)
									lowercase__	     =						ids_tensor([self.batch_size, self.seq_length] ,   self.num_labels						)
									lowercase__	     =						ids_tensor([self.batch_size] ,   2						).float()
									lowercase__	     =						ids_tensor([self.batch_size] ,   self.num_choices						)

							lowercase__	     =						self.get_config()

							return (
							    config,
							    input_ids,
							    token_type_ids,
							    input_lengths,
							    sequence_labels,
							    token_labels,
							    is_impossible_labels,
							    choice_labels,
							    input_mask,
							)

					def     lowerCamelCase__	(self       :		int						)		->  Dict:




							"""simple docstring"""


							return FlaubertConfig(
							    vocab_size=self.vocab_size ,   n_special=self.n_special ,   emb_dim=self.hidden_size ,   n_layers=self.num_hidden_layers ,   n_heads=self.num_attention_heads ,   dropout=self.hidden_dropout_prob ,   attention_dropout=self.attention_probs_dropout_prob ,   gelu_activation=self.gelu_activation ,   sinusoidal_embeddings=self.sinusoidal_embeddings ,   asm=self.asm ,   causal=self.causal ,   n_langs=self.n_langs ,   max_position_embeddings=self.max_position_embeddings ,   initializer_range=self.initializer_range ,   summary_type=self.summary_type ,   use_proj=self.use_proj ,   )

					def     lowerCamelCase__	(self       :		Tuple ,   _UpperCAmelCase       :		List[Any] ,   _UpperCAmelCase       :		List[Any] ,   _UpperCAmelCase       :		Optional[Any] ,   _UpperCAmelCase       :		List[str] ,   _UpperCAmelCase       :		Optional[Any] ,   _UpperCAmelCase       :		Optional[Any] ,   _UpperCAmelCase       :		Union[str, Any] ,   _UpperCAmelCase       :		str ,   _UpperCAmelCase       :		Tuple ,   )		->  Optional[Any]:




							"""simple docstring"""


							lowercase__	     =						FlaubertModel(config=_UpperCAmelCase						)
							model.to(_UpperCAmelCase						)
							model.eval()
							lowercase__	     =						model(_UpperCAmelCase ,   lengths=_UpperCAmelCase ,   langs=_UpperCAmelCase						)
							lowercase__	     =						model(_UpperCAmelCase ,   langs=_UpperCAmelCase						)
							lowercase__	     =						model(_UpperCAmelCase						)
							self.parent.assertEqual(result.last_hidden_state.shape ,   (self.batch_size, self.seq_length, self.hidden_size)						)

					def     lowerCamelCase__	(self       :		Union[str, Any] ,   _UpperCAmelCase       :		Optional[int] ,   _UpperCAmelCase       :		Dict ,   _UpperCAmelCase       :		Any ,   _UpperCAmelCase       :		Tuple ,   _UpperCAmelCase       :		Union[str, Any] ,   _UpperCAmelCase       :		Any ,   _UpperCAmelCase       :		List[Any] ,   _UpperCAmelCase       :		Any ,   _UpperCAmelCase       :		List[str] ,   )		->  Optional[int]:




							"""simple docstring"""


							lowercase__	     =						FlaubertWithLMHeadModel(_UpperCAmelCase						)
							model.to(_UpperCAmelCase						)
							model.eval()

							lowercase__	     =						model(_UpperCAmelCase ,   token_type_ids=_UpperCAmelCase ,   labels=_UpperCAmelCase						)
							self.parent.assertEqual(result.loss.shape ,   ()						)
							self.parent.assertEqual(result.logits.shape ,   (self.batch_size, self.seq_length, self.vocab_size)						)

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




							"""simple docstring"""


							lowercase__	     =						FlaubertForQuestionAnsweringSimple(_UpperCAmelCase						)
							model.to(_UpperCAmelCase						)
							model.eval()

							lowercase__	     =						model(_UpperCAmelCase						)

							lowercase__	     =						model(_UpperCAmelCase ,   start_positions=_UpperCAmelCase ,   end_positions=_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     lowerCamelCase__	(self       :		str ,   _UpperCAmelCase       :		Any ,   _UpperCAmelCase       :		Tuple ,   _UpperCAmelCase       :		Any ,   _UpperCAmelCase       :		Dict ,   _UpperCAmelCase       :		List[Any] ,   _UpperCAmelCase       :		List[Any] ,   _UpperCAmelCase       :		List[Any] ,   _UpperCAmelCase       :		Tuple ,   _UpperCAmelCase       :		Optional[int] ,   )		->  List[Any]:




							"""simple docstring"""


							lowercase__	     =						FlaubertForQuestionAnswering(_UpperCAmelCase						)
							model.to(_UpperCAmelCase						)
							model.eval()

							lowercase__	     =						model(_UpperCAmelCase						)

							lowercase__	     =						model(
							    _UpperCAmelCase ,   start_positions=_UpperCAmelCase ,   end_positions=_UpperCAmelCase ,   cls_index=_UpperCAmelCase ,   is_impossible=_UpperCAmelCase ,   p_mask=_UpperCAmelCase ,   )

							lowercase__	     =						model(
							    _UpperCAmelCase ,   start_positions=_UpperCAmelCase ,   end_positions=_UpperCAmelCase ,   cls_index=_UpperCAmelCase ,   is_impossible=_UpperCAmelCase ,   )

							((lowercase__)     ,      )	     =						result_with_labels.to_tuple()

							lowercase__	     =						model(_UpperCAmelCase ,   start_positions=_UpperCAmelCase ,   end_positions=_UpperCAmelCase						)

							((lowercase__)     ,      )	     =						result_with_labels.to_tuple()

							self.parent.assertEqual(result_with_labels.loss.shape ,   ()						)
							self.parent.assertEqual(result.start_top_log_probs.shape ,   (self.batch_size, model.config.start_n_top)						)
							self.parent.assertEqual(result.start_top_index.shape ,   (self.batch_size, model.config.start_n_top)						)
							self.parent.assertEqual(
							    result.end_top_log_probs.shape ,   (self.batch_size, model.config.start_n_top * model.config.end_n_top)						)
							self.parent.assertEqual(
							    result.end_top_index.shape ,   (self.batch_size, model.config.start_n_top * model.config.end_n_top)						)
							self.parent.assertEqual(result.cls_logits.shape ,   (self.batch_size,)						)

					def     lowerCamelCase__	(self       :		Optional[Any] ,   _UpperCAmelCase       :		Any ,   _UpperCAmelCase       :		Tuple ,   _UpperCAmelCase       :		int ,   _UpperCAmelCase       :		List[Any] ,   _UpperCAmelCase       :		Any ,   _UpperCAmelCase       :		Union[str, Any] ,   _UpperCAmelCase       :		Tuple ,   _UpperCAmelCase       :		Dict ,   _UpperCAmelCase       :		List[str] ,   )		->  List[str]:




							"""simple docstring"""


							lowercase__	     =						FlaubertForSequenceClassification(_UpperCAmelCase						)
							model.to(_UpperCAmelCase						)
							model.eval()

							lowercase__	     =						model(_UpperCAmelCase						)
							lowercase__	     =						model(_UpperCAmelCase ,   labels=_UpperCAmelCase						)

							self.parent.assertEqual(result.loss.shape ,   ()						)
							self.parent.assertEqual(result.logits.shape ,   (self.batch_size, self.type_sequence_label_size)						)

					def     lowerCamelCase__	(self       :		Any ,   _UpperCAmelCase       :		List[str] ,   _UpperCAmelCase       :		Dict ,   _UpperCAmelCase       :		Union[str, Any] ,   _UpperCAmelCase       :		List[str] ,   _UpperCAmelCase       :		str ,   _UpperCAmelCase       :		Dict ,   _UpperCAmelCase       :		int ,   _UpperCAmelCase       :		List[Any] ,   _UpperCAmelCase       :		Optional[Any] ,   )		->  str:




							"""simple docstring"""


							lowercase__	     =						self.num_labels
							lowercase__	     =						FlaubertForTokenClassification(_UpperCAmelCase						)
							model.to(_UpperCAmelCase						)
							model.eval()

							lowercase__	     =						model(_UpperCAmelCase ,   attention_mask=_UpperCAmelCase ,   labels=_UpperCAmelCase						)
							self.parent.assertEqual(result.logits.shape ,   (self.batch_size, self.seq_length, self.num_labels)						)

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




							"""simple docstring"""


							lowercase__	     =						self.num_choices
							lowercase__	     =						FlaubertForMultipleChoice(config=_UpperCAmelCase						)
							model.to(_UpperCAmelCase						)
							model.eval()
							lowercase__	     =						input_ids.unsqueeze(1						).expand(-1 ,   self.num_choices ,   -1						).contiguous()
							lowercase__	     =						token_type_ids.unsqueeze(1						).expand(-1 ,   self.num_choices ,   -1						).contiguous()
							lowercase__	     =						input_mask.unsqueeze(1						).expand(-1 ,   self.num_choices ,   -1						).contiguous()
							lowercase__	     =						model(
							    _UpperCAmelCase ,   attention_mask=_UpperCAmelCase ,   token_type_ids=_UpperCAmelCase ,   labels=_UpperCAmelCase ,   )
							self.parent.assertEqual(result.logits.shape ,   (self.batch_size, self.num_choices)						)


					def     lowerCamelCase__	(self       :		Dict						)		->  Optional[Any]:




							"""simple docstring"""


							lowercase__	     =						self.prepare_config_and_inputs()
							(
							    (
							    lowercase__
							)     ,      (
							    lowercase__
							)     ,      (
							    lowercase__
							)     ,      (
							    lowercase__
							)     ,      (
							    lowercase__
							)     ,      (
							    lowercase__
							)     ,      (
							    lowercase__
							)     ,      (
							    lowercase__
							)     ,      (
							    lowercase__
							)     ,      
							)	     =						config_and_inputs
							lowercase__	     =						{
							    """input_ids""": input_ids,
							    """token_type_ids""": token_type_ids,
							    """lengths""": input_lengths,
							    """attention_mask""": input_mask,
							}
							return config, inputs_dict






@require_torch
class 				A       (			UpperCAmelCase__					,    UpperCAmelCase__					,    unittest.TestCase				):
					'''simple docstring'''






					A__     =					(
					    (
					        FlaubertModel,
					        FlaubertWithLMHeadModel,
					        FlaubertForQuestionAnswering,
					        FlaubertForQuestionAnsweringSimple,
					        FlaubertForSequenceClassification,
					        FlaubertForTokenClassification,
					        FlaubertForMultipleChoice,
					    )
					    if is_torch_available()
					    else ()
					)
					A__     =					(
					    {
					        '''feature-extraction''': FlaubertModel,
					        '''fill-mask''': FlaubertWithLMHeadModel,
					        '''question-answering''': FlaubertForQuestionAnsweringSimple,
					        '''text-classification''': FlaubertForSequenceClassification,
					        '''token-classification''': FlaubertForTokenClassification,
					        '''zero-shot''': FlaubertForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)

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




							"""simple docstring"""


							if (
							    pipeline_test_casse_name == "QAPipelineTests"
							    and tokenizer_name is not None
							    and not tokenizer_name.endswith("""Fast"""						)
							):
									# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
									# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
									# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
									return True

							return False

					def     lowerCamelCase__	(self       :		Any ,   _UpperCAmelCase       :		int ,   _UpperCAmelCase       :		List[str] ,   _UpperCAmelCase       :		Union[str, Any]=False						)		->  Optional[int]:




							"""simple docstring"""


							lowercase__	     =						super()._prepare_for_class(_UpperCAmelCase ,   _UpperCAmelCase ,   return_labels=_UpperCAmelCase						)

							if return_labels:
									if model_class.__name__ == "FlaubertForQuestionAnswering":
											lowercase__	     =						torch.zeros(
											    self.model_tester.batch_size ,   dtype=torch.long ,   device=_UpperCAmelCase						)
											lowercase__	     =						torch.zeros(
											    self.model_tester.batch_size ,   dtype=torch.long ,   device=_UpperCAmelCase						)

							return inputs_dict

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




							"""simple docstring"""


							lowercase__	     =						FlaubertModelTester(self						)
							lowercase__	     =						ConfigTester(self ,   config_class=_UpperCAmelCase ,   emb_dim=37						)

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




							"""simple docstring"""


							self.config_tester.run_common_tests()

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




							"""simple docstring"""


							lowercase__	     =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_flaubert_model(*_UpperCAmelCase						)

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




							"""simple docstring"""


							lowercase__	     =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_flaubert_lm_head(*_UpperCAmelCase						)

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




							"""simple docstring"""


							lowercase__	     =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_flaubert_simple_qa(*_UpperCAmelCase						)

					def     lowerCamelCase__	(self       :		str						)		->  Any:




							"""simple docstring"""


							lowercase__	     =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_flaubert_qa(*_UpperCAmelCase						)

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




							"""simple docstring"""


							lowercase__	     =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_flaubert_sequence_classif(*_UpperCAmelCase						)

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




							"""simple docstring"""


							lowercase__	     =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_flaubert_token_classif(*_UpperCAmelCase						)

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




							"""simple docstring"""


							lowercase__	     =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_flaubert_multiple_choice(*_UpperCAmelCase						)

					@slow
					def     lowerCamelCase__	(self       :		int						)		->  List[Any]:




							"""simple docstring"""


							for model_name in FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									lowercase__	     =						FlaubertModel.from_pretrained(_UpperCAmelCase						)
									self.assertIsNotNone(_UpperCAmelCase						)


					@slow
					@require_torch_gpu
					def     lowerCamelCase__	(self       :		Dict						)		->  int:




							"""simple docstring"""


							lowercase__     ,      lowercase__	     =						self.model_tester.prepare_config_and_inputs_for_common()
							for model_class in self.all_model_classes:
									# FlauBertForMultipleChoice behaves incorrectly in JIT environments.
									if model_class == FlaubertForMultipleChoice:
											return

									lowercase__	     =						True
									lowercase__	     =						model_class(config=_UpperCAmelCase						)

									lowercase__	     =						self._prepare_for_class(_UpperCAmelCase ,   _UpperCAmelCase						)
									lowercase__	     =						torch.jit.trace(
									    _UpperCAmelCase ,   (inputs_dict["""input_ids"""].to("""cpu"""						), inputs_dict["""attention_mask"""].to("""cpu"""						))						)

									with tempfile.TemporaryDirectory() as tmp:
											torch.jit.save(_UpperCAmelCase ,   os.path.join(_UpperCAmelCase ,   """traced_model.pt"""						)						)
											lowercase__	     =						torch.jit.load(os.path.join(_UpperCAmelCase ,   """traced_model.pt"""						) ,   map_location=_UpperCAmelCase						)
											loaded(inputs_dict["""input_ids"""].to(_UpperCAmelCase						) ,   inputs_dict["""attention_mask"""].to(_UpperCAmelCase						)						)






@require_torch
class 				A       (			unittest.TestCase				):
					'''simple docstring'''






					@slow
					def     lowerCamelCase__	(self       :		Tuple						)		->  Dict:




							"""simple docstring"""


							lowercase__	     =						FlaubertModel.from_pretrained("""flaubert/flaubert_base_cased"""						)
							lowercase__	     =						torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]						)
							with torch.no_grad():
									lowercase__	     =						model(_UpperCAmelCase						)[0]
							lowercase__	     =						torch.Size((1, 11, 768)						)
							self.assertEqual(output.shape ,   _UpperCAmelCase						)
							lowercase__	     =						torch.tensor(
							    [[[-2.6_251, -1.4_298, -0.0_227], [-2.8_510, -1.6_387, 0.2_258], [-2.8_114, -1.1_832, -0.3_066]]]						)

							self.assertTrue(torch.allclose(output[:, :3, :3] ,   _UpperCAmelCase ,   atol=1E-4						)						)

