



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       :



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


           '''simple docstring'''

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



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


           '''simple docstring'''

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

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

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

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

           __a						       =				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=__SCREAMING_SNAKE_CASE     ,		)

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



       def 		_lowerCamelCase			(		self							:     List[Any]     ,		__SCREAMING_SNAKE_CASE							:     List[Any]     ,		__SCREAMING_SNAKE_CASE							:     Tuple     ,		__SCREAMING_SNAKE_CASE							:     List[Any]     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]     ,		__SCREAMING_SNAKE_CASE							:     Dict     ,		__SCREAMING_SNAKE_CASE							:     Tuple):


           '''simple docstring'''

           __a						       =				TFDebertaVaModel(config=__SCREAMING_SNAKE_CASE)
           __a						       =				{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}

           __a						       =				[input_ids, input_mask]
           __a						       =				model(__SCREAMING_SNAKE_CASE)

           __a						       =				model(__SCREAMING_SNAKE_CASE)

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



       def 		_lowerCamelCase			(		self							:     List[Any]     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]     ,		__SCREAMING_SNAKE_CASE							:     Dict     ,		__SCREAMING_SNAKE_CASE							:     List[str]     ,		__SCREAMING_SNAKE_CASE							:     int     ,		__SCREAMING_SNAKE_CASE							:     Any     ,		__SCREAMING_SNAKE_CASE							:     str     ,		__SCREAMING_SNAKE_CASE							:     Dict):


           '''simple docstring'''

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



       def 		_lowerCamelCase			(		self							:     Tuple     ,		__SCREAMING_SNAKE_CASE							:     int     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]     ,		__SCREAMING_SNAKE_CASE							:     Tuple     ,		__SCREAMING_SNAKE_CASE							:     int     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]     ,		__SCREAMING_SNAKE_CASE							:     int):


           '''simple docstring'''

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

           __a						       =				model(__SCREAMING_SNAKE_CASE)
           self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, self.num_labels))



       def 		_lowerCamelCase			(		self							:     Dict     ,		__SCREAMING_SNAKE_CASE							:     str     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]     ,		__SCREAMING_SNAKE_CASE							:     List[str]     ,		__SCREAMING_SNAKE_CASE							:     List[str]     ,		__SCREAMING_SNAKE_CASE							:     str):


           '''simple docstring'''

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



       def 		_lowerCamelCase			(		self							:     Tuple     ,		__SCREAMING_SNAKE_CASE							:     int     ,		__SCREAMING_SNAKE_CASE							:     List[str]     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]     ,		__SCREAMING_SNAKE_CASE							:     int     ,		__SCREAMING_SNAKE_CASE							:     Any     ,		__SCREAMING_SNAKE_CASE							:     Dict     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]):


           '''simple docstring'''

           __a						       =				TFDebertaVaForQuestionAnswering(config=__SCREAMING_SNAKE_CASE)
           __a						       =				{
               '''input_ids''': input_ids,
               '''attention_mask''': input_mask,
               '''token_type_ids''': token_type_ids,
           }

           __a						       =				model(__SCREAMING_SNAKE_CASE)
           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							:     Dict):


           '''simple docstring'''

           __a						       =				self.prepare_config_and_inputs()
           (
               (
               __a
           )       ,   (
               __a
           )       ,   (
               __a
           )       ,   (
               __a
           )       ,   (
               __a
           )       ,   (
               __a
           )       ,   (
               __a
           )       ,   
           )						       =				config_and_inputs
           __a						       =				{'''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    ):
       UpperCamelCase__  :    Dict											=						(
           (
               TFDebertaVaModel,
               TFDebertaVaForMaskedLM,
               TFDebertaVaForQuestionAnswering,
               TFDebertaVaForSequenceClassification,
               TFDebertaVaForTokenClassification,
           )
           if is_tf_available()
           else ()
       )
       UpperCamelCase__  :    Optional[int]											=						(
           {
               '''feature-extraction''': TFDebertaVaModel,
               '''fill-mask''': TFDebertaVaForMaskedLM,
               '''question-answering''': TFDebertaVaForQuestionAnswering,
               '''text-classification''': TFDebertaVaForSequenceClassification,
               '''token-classification''': TFDebertaVaForTokenClassification,
               '''zero-shot''': TFDebertaVaForSequenceClassification,
           }
           if is_tf_available()
           else {}
       )

       UpperCamelCase__  :    int											=						False
       UpperCamelCase__  :    Optional[int]											=						False



       def 		_lowerCamelCase			(		self							:     Optional[Any]):


           '''simple docstring'''

           __a						       =				TFDebertaVaModelTester(self)
           __a						       =				ConfigTester(self     ,		config_class=__SCREAMING_SNAKE_CASE     ,		hidden_size=37)



       def 		_lowerCamelCase			(		self							:     List[str]):


           '''simple docstring'''

           self.config_tester.run_common_tests()



       def 		_lowerCamelCase			(		self							:     Optional[int]):


           '''simple docstring'''

           __a						       =				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     List[Any]):


           '''simple docstring'''

           __a						       =				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_lm(*__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     List[Any]):


           '''simple docstring'''

           __a						       =				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_question_answering(*__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     List[str]):


           '''simple docstring'''

           __a						       =				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_sequence_classification(*__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     int):


           '''simple docstring'''

           __a						       =				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_token_classification(*__SCREAMING_SNAKE_CASE)





       @slow
       def 		_lowerCamelCase			(		self							:     List[str]):


           '''simple docstring'''

           __a						       =				TFDebertaVaModel.from_pretrained('''kamalkraj/deberta-v2-xlarge''')
           self.assertIsNotNone(__SCREAMING_SNAKE_CASE)


@require_tf
class     _A       (	unittest.TestCase    ):



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


           '''simple docstring'''

           pass





       @slow
       def 		_lowerCamelCase			(		self							:     Any):


           '''simple docstring'''

           __a						       =				TFDebertaVaModel.from_pretrained('''kamalkraj/deberta-v2-xlarge''')
           __a						       =				tf.constant([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]])
           __a						       =				tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
           __a						       =				model(__SCREAMING_SNAKE_CASE     ,		attention_mask=__SCREAMING_SNAKE_CASE)[0]

           __a						       =				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]     ,		__SCREAMING_SNAKE_CASE     ,		atol=1E-4)










'''simple docstring'''




import unittest

import torch
from torch import nn

from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory



def       __lowerCamelCase  (					)      ->			Any:
							raise RuntimeError("""CUDA out of memory."""		)

class 			UpperCamelCase_ (							nn.Module			):





	def __init__(  self    )			->			Any:
								super().__init__()
								UpperCAmelCase     :		Tuple							     =		nn.Linear(3		,		4    )
								UpperCAmelCase     :		Tuple							     =		nn.BatchNormad(4    )
								UpperCAmelCase     :		int							     =		nn.Linear(4		,		5    )






	def  _lowercase(  self		,		A    )			->			Any:
								return self.lineara(self.batchnorm(self.lineara(A    )    )    )




class 			UpperCamelCase_ (							unittest.TestCase			):





	def  _lowercase(  self    )			->			Optional[Any]:
								UpperCAmelCase     :		Optional[int]							     =		[]

								@find_executable_batch_size(starting_batch_size=128    )
								def mock_training_loop_function(A    ):
															nonlocal batch_sizes
															batch_sizes.append(A    )
															if batch_size != 8:
																						raise_fake_out_of_memory()

								mock_training_loop_function()
								self.assertListEqual(A		,		[128, 64, 32, 16, 8]    )





	def  _lowercase(  self    )			->			Any:
								UpperCAmelCase     :		Optional[Any]							     =		[]

								@find_executable_batch_size(starting_batch_size=128    )
								def mock_training_loop_function(A		,		A    ):
															nonlocal batch_sizes
															batch_sizes.append(A    )
															if batch_size != 8:
																						raise_fake_out_of_memory()
															return batch_size, arga

								UpperCAmelCase     ,     UpperCAmelCase     :		Optional[int]							     =		mock_training_loop_function("""hello"""    )
								self.assertListEqual(A		,		[128, 64, 32, 16, 8]    )
								self.assertListEqual([bs, arga]		,		[8, """hello"""]    )





	def  _lowercase(  self    )			->			Any:
								@find_executable_batch_size(starting_batch_size=0    )
								def mock_training_loop_function(A    ):
															pass

								with self.assertRaises(A    ) as cm:
															mock_training_loop_function()
															self.assertIn("""No executable batch size found, reached zero."""		,		cm.exception.args[0]    )





	def  _lowercase(  self    )			->			Optional[int]:
								@find_executable_batch_size(starting_batch_size=16    )
								def mock_training_loop_function(A    ):
															if batch_size > 0:
																						raise_fake_out_of_memory()
															pass

								with self.assertRaises(A    ) as cm:
															mock_training_loop_function()
															self.assertIn("""No executable batch size found, reached zero."""		,		cm.exception.args[0]    )





	def  _lowercase(  self    )			->			Optional[Any]:
								@find_executable_batch_size(starting_batch_size=128    )
								def mock_training_loop_function(A		,		A		,		A    ):
															if batch_size != 8:
																						raise raise_fake_out_of_memory()

								with self.assertRaises(A    ) as cm:
															mock_training_loop_function(128		,		"""hello"""		,		"""world"""    )
															self.assertIn("""Batch size was passed into `f`"""		,		cm.exception.args[0]    )
															self.assertIn("""`f(arg1='hello', arg2='world')"""		,		cm.exception.args[0]    )





	def  _lowercase(  self    )			->			int:
								@find_executable_batch_size(starting_batch_size=16    )
								def mock_training_loop_function(A    ):
															raise ValueError("""Oops, we had an error!"""    )

								with self.assertRaises(A    ) as cm:
															mock_training_loop_function()
															self.assertIn("""Oops, we had an error!"""		,		cm.exception.args[0]    )






	@require_cuda
	def  _lowercase(  self    )			->			Optional[int]:
								UpperCAmelCase     :		Optional[Any]							     =		torch.cuda.memory_allocated()
								UpperCAmelCase     :		List[str]							     =		ModelForTest()
								model.cuda()
								self.assertGreater(torch.cuda.memory_allocated()		,		A    )
								UpperCAmelCase     :		Tuple							     =		release_memory(A    )
								self.assertEqual(torch.cuda.memory_allocated()		,		A    )

