
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					:







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







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		List[str]	   =     ids_tensor([self.batch_size, self.seq_length]						,   self.vocab_size		)

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

				__magic_name__		:		Optional[int]	   =     None
				if self.use_token_type_ids:
					__magic_name__		:		Tuple	   =     ids_tensor([self.batch_size, self.seq_length]						,   self.type_vocab_size		)

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

				__magic_name__		:		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 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a						,   _a						,   _a						,   _a						,   _a						,   _a		):
				__magic_name__		:		List[Any]	   =     TFDebertaVaModel(config=_a		)
				__magic_name__		:		Optional[int]	   =     {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

				__magic_name__		:		Union[str, Any]	   =     [input_ids, input_mask]
				__magic_name__		:		Any	   =     model(_a		)

				__magic_name__		:		Optional[Any]	   =     model(_a		)

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







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a						,   _a						,   _a						,   _a						,   _a						,   _a		):
				__magic_name__		:		Any	   =     TFDebertaVaForMaskedLM(config=_a		)
				__magic_name__		:		Optional[Any]	   =     {
				    "input_ids": input_ids,
				    "attention_mask": input_mask,
				    "token_type_ids": token_type_ids,
				}
				__magic_name__		:		Dict	   =     model(_a		)
				self.parent.assertEqual(result.logits.shape						,   (self.batch_size, self.seq_length, self.vocab_size)		)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a						,   _a						,   _a						,   _a						,   _a						,   _a		):
				__magic_name__		:		Optional[int]	   =     self.num_labels
				__magic_name__		:		str	   =     TFDebertaVaForSequenceClassification(config=_a		)
				__magic_name__		:		Optional[Any]	   =     {
				    "input_ids": input_ids,
				    "attention_mask": input_mask,
				    "token_type_ids": token_type_ids,
				}

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







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







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a						,   _a						,   _a						,   _a						,   _a						,   _a		):
				__magic_name__		:		Union[str, Any]	   =     TFDebertaVaForQuestionAnswering(config=_a		)
				__magic_name__		:		Optional[Any]	   =     {
				    "input_ids": input_ids,
				    "attention_mask": input_mask,
				    "token_type_ids": token_type_ids,
				}

				__magic_name__		:		Optional[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 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Dict	   =     self.prepare_config_and_inputs()
				(
				    (
				    __magic_name__
				)   ,    (
				    __magic_name__
				)   ,    (
				    __magic_name__
				)   ,    (
				    __magic_name__
				)   ,    (
				    __magic_name__
				)   ,    (
				    __magic_name__
				)   ,    (
				    __magic_name__
				)   ,    
				)		:		Tuple	   =     config_and_inputs
				__magic_name__		:		Optional[int]	   =     {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
				return config, inputs_dict







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

			UpperCamelCase__          =    False
			UpperCamelCase__          =    False







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Dict	   =     TFDebertaVaModelTester(self		)
				__magic_name__		:		List[Any]	   =     ConfigTester(self						,   config_class=_a						,   hidden_size=37		)







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







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Optional[int]	   =     self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*_a		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		List[Any]	   =     self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_masked_lm(*_a		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		int	   =     self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_question_answering(*_a		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Tuple	   =     self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_sequence_classification(*_a		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Optional[Any]	   =     self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_token_classification(*_a		)







			@slow
			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Union[str, Any]	   =     TFDebertaVaModel.from_pretrained("kamalkraj/deberta-v2-xlarge"		)
				self.assertIsNotNone(_a		)







@require_tf
class       _snake_case					(     unittest.TestCase				):







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







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

				__magic_name__		:		Any	   =     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]						,   _a						,   atol=1e-4		)


from __future__ import annotations





class       _snake_case					:







			def __init__(				self						,   _a		):
				__magic_name__		:		Optional[Any]	   =     data
				__magic_name__		:		Node | None	   =     None
				__magic_name__		:		Node | None	   =     None







def   lowerCAmelCase_						(					_snake_case      :  Node | None   )				->		None:  # In Order traversal of the tree







	'''simple docstring'''


	if tree:
		display(tree.left   )
		print(tree.data   )
		display(tree.right   )






def   lowerCAmelCase_						(					_snake_case      :  Node | None   )				->		int:







	'''simple docstring'''


	return 1 + max(depth_of_tree(tree.left   )      ,   depth_of_tree(tree.right   )   ) if tree else 0






def   lowerCAmelCase_						(					_snake_case      :  Node   )				->		bool:







	'''simple docstring'''


	if not tree:
		return True
	if tree.left and tree.right:
		return is_full_binary_tree(tree.left   ) and is_full_binary_tree(tree.right   )
	else:
		return not tree.left and not tree.right






def   lowerCAmelCase_						(					)				->		None:  # Main function for testing.







	'''simple docstring'''


	__magic_name__		:		int	   =     Node(1   )
	__magic_name__		:		Union[str, Any]	   =     Node(2   )
	__magic_name__		:		Tuple	   =     Node(3   )
	__magic_name__		:		Optional[Any]	   =     Node(4   )
	__magic_name__		:		Union[str, Any]	   =     Node(5   )
	__magic_name__		:		Any	   =     Node(6   )
	__magic_name__		:		int	   =     Node(7   )
	__magic_name__		:		List[str]	   =     Node(8   )
	__magic_name__		:		Union[str, Any]	   =     Node(9   )

	print(is_full_binary_tree(_snake_case   )   )
	print(depth_of_tree(_snake_case   )   )
	print("Tree is: "   )
	display(_snake_case   )


if __name__ == "__main__":
				main()
