



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 				snake_case__:

					"""simple docstring"""






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







					def 			snake_case					(							self :     Dict					):
									lowercase__					:   List[str]													=					ids_tensor([self.batch_size, self.seq_length]				,       self.vocab_size					)

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

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

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

									lowercase__					:   Optional[int]													=					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 			snake_case					(							self :     Tuple				,       SCREAMING_SNAKE_CASE :     str				,       SCREAMING_SNAKE_CASE :     Dict				,       SCREAMING_SNAKE_CASE :     int				,       SCREAMING_SNAKE_CASE :     Dict				,       SCREAMING_SNAKE_CASE :     Optional[int]				,       SCREAMING_SNAKE_CASE :     Optional[Any]				,       SCREAMING_SNAKE_CASE :     Optional[Any]					):
									lowercase__					:   Optional[Any]													=					TFDebertaVaModel(config=a_					)
									lowercase__					:   List[Any]													=					{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

									lowercase__					:   Dict													=					[input_ids, input_mask]
									lowercase__					:   str													=					model(a_					)

									lowercase__					:   Optional[int]													=					model(a_					)

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







					def 			snake_case					(							self :     Dict				,       SCREAMING_SNAKE_CASE :     List[str]				,       SCREAMING_SNAKE_CASE :     Optional[int]				,       SCREAMING_SNAKE_CASE :     Tuple				,       SCREAMING_SNAKE_CASE :     int				,       SCREAMING_SNAKE_CASE :     int				,       SCREAMING_SNAKE_CASE :     Tuple				,       SCREAMING_SNAKE_CASE :     Union[str, Any]					):
									lowercase__					:   int													=					TFDebertaVaForMaskedLM(config=a_					)
									lowercase__					:   List[str]													=					{
									    "input_ids": input_ids,
									    "attention_mask": input_mask,
									    "token_type_ids": token_type_ids,
									}
									lowercase__					:   Tuple													=					model(a_					)
									self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.seq_length, self.vocab_size)					)







					def 			snake_case					(							self :     int				,       SCREAMING_SNAKE_CASE :     str				,       SCREAMING_SNAKE_CASE :     List[str]				,       SCREAMING_SNAKE_CASE :     Dict				,       SCREAMING_SNAKE_CASE :     List[Any]				,       SCREAMING_SNAKE_CASE :     Optional[int]				,       SCREAMING_SNAKE_CASE :     Optional[Any]				,       SCREAMING_SNAKE_CASE :     Any					):
									lowercase__					:   str													=					self.num_labels
									lowercase__					:   List[Any]													=					TFDebertaVaForSequenceClassification(config=a_					)
									lowercase__					:   Optional[Any]													=					{
									    "input_ids": input_ids,
									    "attention_mask": input_mask,
									    "token_type_ids": token_type_ids,
									}

									lowercase__					:   List[str]													=					model(a_					)
									self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.num_labels)					)







					def 			snake_case					(							self :     Dict				,       SCREAMING_SNAKE_CASE :     List[Any]				,       SCREAMING_SNAKE_CASE :     Optional[int]				,       SCREAMING_SNAKE_CASE :     Dict				,       SCREAMING_SNAKE_CASE :     List[str]				,       SCREAMING_SNAKE_CASE :     Optional[Any]				,       SCREAMING_SNAKE_CASE :     Tuple				,       SCREAMING_SNAKE_CASE :     Dict					):
									lowercase__					:   str													=					self.num_labels
									lowercase__					:   Union[str, Any]													=					TFDebertaVaForTokenClassification(config=a_					)
									lowercase__					:   Optional[Any]													=					{
									    "input_ids": input_ids,
									    "attention_mask": input_mask,
									    "token_type_ids": token_type_ids,
									}
									lowercase__					:   Union[str, Any]													=					model(a_					)
									self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.seq_length, self.num_labels)					)







					def 			snake_case					(							self :     List[str]				,       SCREAMING_SNAKE_CASE :     Any				,       SCREAMING_SNAKE_CASE :     Any				,       SCREAMING_SNAKE_CASE :     Dict				,       SCREAMING_SNAKE_CASE :     Optional[int]				,       SCREAMING_SNAKE_CASE :     Any				,       SCREAMING_SNAKE_CASE :     int				,       SCREAMING_SNAKE_CASE :     Dict					):
									lowercase__					:   Tuple													=					TFDebertaVaForQuestionAnswering(config=a_					)
									lowercase__					:   Optional[Any]													=					{
									    "input_ids": input_ids,
									    "attention_mask": input_mask,
									    "token_type_ids": token_type_ids,
									}

									lowercase__					:   List[Any]													=					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 			snake_case					(							self :     Union[str, Any]					):
									lowercase__					:   str													=					self.prepare_config_and_inputs()
									(
									    (
									    lowercase__						
									)							,							(
									    lowercase__						
									)							,							(
									    lowercase__						
									)							,							(
									    lowercase__						
									)							,							(
									    lowercase__						
									)							,							(
									    lowercase__						
									)							,							(
									    lowercase__						
									)							,													
									)					:   str													=					config_and_inputs
									lowercase__					:   Any													=					{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
									return config, inputs_dict






@require_tf
class 				snake_case__(SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__    ,      unittest.TestCase      ):

					"""simple docstring"""






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

					lowercase_								=		False
					lowercase_								=		False







					def 			snake_case					(							self :     Dict					):
									lowercase__					:   Optional[Any]													=					TFDebertaVaModelTester(self					)
									lowercase__					:   Optional[int]													=					ConfigTester(self				,       config_class=a_				,       hidden_size=37					)







					def 			snake_case					(							self :     Any					):
									self.config_tester.run_common_tests()







					def 			snake_case					(							self :     Optional[int]					):
									lowercase__					:   Dict													=					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*a_					)







					def 			snake_case					(							self :     Optional[Any]					):
									lowercase__					:   List[str]													=					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_lm(*a_					)







					def 			snake_case					(							self :     int					):
									lowercase__					:   List[Any]													=					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_question_answering(*a_					)







					def 			snake_case					(							self :     List[Any]					):
									lowercase__					:   Union[str, Any]													=					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_sequence_classification(*a_					)







					def 			snake_case					(							self :     Dict					):
									lowercase__					:   Optional[int]													=					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_token_classification(*a_					)







					@slow
					def 			snake_case					(							self :     Any					):
									lowercase__					:   Any													=					TFDebertaVaModel.from_pretrained("kamalkraj/deberta-v2-xlarge"					)
									self.assertIsNotNone(a_					)






@require_tf
class 				snake_case__(unittest.TestCase      ):

					"""simple docstring"""






					@unittest.skip(reason="Model not available yet"					)
					def 			snake_case					(							self :     Any					):
									pass







					@slow
					def 			snake_case					(							self :     Union[str, Any]					):
									lowercase__					:   Optional[Any]													=					TFDebertaVaModel.from_pretrained("kamalkraj/deberta-v2-xlarge"					)
									lowercase__					:   str													=					tf.constant([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]					)
									lowercase__					:   Any													=					tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]					)
									lowercase__					:   Optional[int]													=					model(a_				,       attention_mask=a_					)[0]

									lowercase__					:   Any													=					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]				,       a_				,       atol=1E-4					)









from math import ceil, sqrt


def  __lowerCamelCase (       lowerCamelCase__ = 1_000_000						):

				"""simple docstring"""


				lowercase__					:   int													=					0

				for outer_width in range(3					,			(limit // 4) + 2						):
								if outer_width**2 > limit:
												lowercase__					:   List[str]													=					max(ceil(sqrt(outer_width**2 - limit						)						)					,			1						)
								else:
												lowercase__					:   List[str]													=					1
								if (outer_width - hole_width_lower_bound) % 2:
												hole_width_lower_bound += 1

								answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1

				return answer


if __name__ == "__main__":
				print(f'''{solution() = }''')




