


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				,     lowercase__				,     lowercase__=1_3				,     lowercase__=7				,     lowercase__=True				,     lowercase__=True				,     lowercase__=True				,     lowercase__=True				,     lowercase__=9_9				,     lowercase__=3_2				,     lowercase__=2				,     lowercase__=4				,     lowercase__=3_7				,     lowercase__="gelu"				,     lowercase__=0.1				,     lowercase__=0.1				,     lowercase__=5_1_2				,     lowercase__=1_6				,     lowercase__=2				,     lowercase__=0.0_2				,     lowercase__=False				,     lowercase__=True				,     lowercase__="None"				,     lowercase__=3				,     lowercase__=4				,     lowercase__=None				,     ):
							__UpperCAmelCase			:							str	    =	parent
							__UpperCAmelCase			:							str	    =	batch_size
							__UpperCAmelCase			:							Tuple	    =	seq_length
							__UpperCAmelCase			:							Tuple	    =	is_training
							__UpperCAmelCase			:							Optional[Any]	    =	use_input_mask
							__UpperCAmelCase			:							Any	    =	use_token_type_ids
							__UpperCAmelCase			:							Optional[Any]	    =	use_labels
							__UpperCAmelCase			:							int	    =	vocab_size
							__UpperCAmelCase			:							Tuple	    =	hidden_size
							__UpperCAmelCase			:							List[Any]	    =	num_hidden_layers
							__UpperCAmelCase			:							List[str]	    =	num_attention_heads
							__UpperCAmelCase			:							Any	    =	intermediate_size
							__UpperCAmelCase			:							Dict	    =	hidden_act
							__UpperCAmelCase			:							str	    =	hidden_dropout_prob
							__UpperCAmelCase			:							Optional[Any]	    =	attention_probs_dropout_prob
							__UpperCAmelCase			:							int	    =	max_position_embeddings
							__UpperCAmelCase			:							Tuple	    =	type_vocab_size
							__UpperCAmelCase			:							Any	    =	type_sequence_label_size
							__UpperCAmelCase			:							str	    =	initializer_range
							__UpperCAmelCase			:							Union[str, Any]	    =	num_labels
							__UpperCAmelCase			:							str	    =	num_choices
							__UpperCAmelCase			:							List[str]	    =	relative_attention
							__UpperCAmelCase			:							Dict	    =	position_biased_input
							__UpperCAmelCase			:							List[Any]	    =	pos_att_type
							__UpperCAmelCase			:							Tuple	    =	scope


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

							__UpperCAmelCase			:							Dict	    =	None
							if self.use_input_mask:
													__UpperCAmelCase			:							Union[str, Any]	    =	random_attention_mask([self.batch_size, self.seq_length])

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

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

							__UpperCAmelCase			:							List[Any]	    =	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=lowercase__				,     )

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


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__):
							__UpperCAmelCase			:							Tuple	    =	TFDebertaVaModel(config=lowercase__)
							__UpperCAmelCase			:							Optional[Any]	    =	{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}

							__UpperCAmelCase			:							Any	    =	[input_ids, input_mask]
							__UpperCAmelCase			:							Optional[int]	    =	model(lowercase__)

							__UpperCAmelCase			:							List[str]	    =	model(lowercase__)

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


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__):
							__UpperCAmelCase			:							str	    =	TFDebertaVaForMaskedLM(config=lowercase__)
							__UpperCAmelCase			:							List[Any]	    =	{
							    '''input_ids''': input_ids,
							    '''attention_mask''': input_mask,
							    '''token_type_ids''': token_type_ids,
							}
							__UpperCAmelCase			:							List[Any]	    =	model(lowercase__)
							self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.seq_length, self.vocab_size))


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__):
							__UpperCAmelCase			:							Tuple	    =	self.num_labels
							__UpperCAmelCase			:							Union[str, Any]	    =	TFDebertaVaForSequenceClassification(config=lowercase__)
							__UpperCAmelCase			:							str	    =	{
							    '''input_ids''': input_ids,
							    '''attention_mask''': input_mask,
							    '''token_type_ids''': token_type_ids,
							}

							__UpperCAmelCase			:							Optional[int]	    =	model(lowercase__)
							self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.num_labels))


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__):
							__UpperCAmelCase			:							int	    =	self.num_labels
							__UpperCAmelCase			:							str	    =	TFDebertaVaForTokenClassification(config=lowercase__)
							__UpperCAmelCase			:							Dict	    =	{
							    '''input_ids''': input_ids,
							    '''attention_mask''': input_mask,
							    '''token_type_ids''': token_type_ids,
							}
							__UpperCAmelCase			:							int	    =	model(lowercase__)
							self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.seq_length, self.num_labels))


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__):
							__UpperCAmelCase			:							Optional[int]	    =	TFDebertaVaForQuestionAnswering(config=lowercase__)
							__UpperCAmelCase			:							str	    =	{
							    '''input_ids''': input_ids,
							    '''attention_mask''': input_mask,
							    '''token_type_ids''': token_type_ids,
							}

							__UpperCAmelCase			:							Tuple	    =	model(lowercase__)
							self.parent.assertEqual(result.start_logits.shape				,     (self.batch_size, self.seq_length))
							self.parent.assertEqual(result.end_logits.shape				,     (self.batch_size, self.seq_length))


	def  A(    self):
							__UpperCAmelCase			:							Optional[int]	    =	self.prepare_config_and_inputs()
							(
							    (
							    __UpperCAmelCase
							)    ,      (
							    __UpperCAmelCase
							)    ,      (
							    __UpperCAmelCase
							)    ,      (
							    __UpperCAmelCase
							)    ,      (
							    __UpperCAmelCase
							)    ,      (
							    __UpperCAmelCase
							)    ,      (
							    __UpperCAmelCase
							)    ,      
							)			:							str	    =	config_and_inputs
							__UpperCAmelCase			:							int	    =	{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
							return config, inputs_dict






@require_tf
class 							lowerCamelCase   (  _UpperCamelCase							,						_UpperCamelCase							,						unittest.TestCase ):
	_lowerCAmelCase     :		Tuple										=						(
	    (
	        TFDebertaVaModel,
	        TFDebertaVaForMaskedLM,
	        TFDebertaVaForQuestionAnswering,
	        TFDebertaVaForSequenceClassification,
	        TFDebertaVaForTokenClassification,
	    )
	    if is_tf_available()
	    else ()
	)
	_lowerCAmelCase     :		Union[str, 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     :		Optional[Any]										=						False


	def  A(    self):
							__UpperCAmelCase			:							str	    =	TFDebertaVaModelTester(self)
							__UpperCAmelCase			:							Tuple	    =	ConfigTester(self				,     config_class=lowercase__				,     hidden_size=3_7)


	def  A(    self):
							self.config_tester.run_common_tests()


	def  A(    self):
							__UpperCAmelCase			:							str	    =	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*lowercase__)


	def  A(    self):
							__UpperCAmelCase			:							List[Any]	    =	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*lowercase__)


	def  A(    self):
							__UpperCAmelCase			:							Optional[int]	    =	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*lowercase__)


	def  A(    self):
							__UpperCAmelCase			:							Optional[int]	    =	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*lowercase__)


	def  A(    self):
							__UpperCAmelCase			:							List[str]	    =	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*lowercase__)


	@slow
	def  A(    self):
							__UpperCAmelCase			:							Any	    =	TFDebertaVaModel.from_pretrained('''kamalkraj/deberta-v2-xlarge''')
							self.assertIsNotNone(lowercase__)






@require_tf
class 							lowerCamelCase   (  unittest.TestCase ):


	@unittest.skip(reason='''Model not available yet''')
	def  A(    self):
							pass


	@slow
	def  A(    self):
							__UpperCAmelCase			:							Dict	    =	TFDebertaVaModel.from_pretrained('''kamalkraj/deberta-v2-xlarge''')
							__UpperCAmelCase			:							Union[str, Any]	    =	tf.constant([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]])
							__UpperCAmelCase			:							Tuple	    =	tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
							__UpperCAmelCase			:							List[str]	    =	model(lowercase__				,     attention_mask=lowercase__)[0]

							__UpperCAmelCase			:							List[Any]	    =	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]				,     lowercase__				,     atol=1e-4)





import unittest

from knapsack import greedy_knapsack as kp




class 							lowerCamelCase   (  unittest.TestCase ):


	def  A(    self):
							__UpperCAmelCase			:							Optional[Any]	    =	[1_0, 2_0, 3_0, 4_0, 5_0, 6_0]
							__UpperCAmelCase			:							str	    =	[2, 4, 6, 8, 1_0, 1_2]
							__UpperCAmelCase			:							List[Any]	    =	1_0_0
							self.assertEqual(kp.calc_profit(lowercase__				,     lowercase__				,     lowercase__)				,     2_1_0)


	def  A(    self):
							self.assertRaisesRegex(lowercase__				,     '''max_weight must greater than zero.''')


	def  A(    self):
							self.assertRaisesRegex(lowercase__				,     '''Weight can not be negative.''')


	def  A(    self):
							self.assertRaisesRegex(lowercase__				,     '''Profit can not be negative.''')


	def  A(    self):
							self.assertRaisesRegex(lowercase__				,     '''max_weight must greater than zero.''')


	def  A(    self):
							self.assertRaisesRegex(
							    lowercase__				,     '''The length of profit and weight must be same.''')


if __name__ == "__main__":
				unittest.main()

