

'''simple docstring'''
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			,_snake_case			,_snake_case=13			,_snake_case=7			,_snake_case=True			,_snake_case=True			,_snake_case=True			,_snake_case=True			,_snake_case=99			,_snake_case=32			,_snake_case=2			,_snake_case=4			,_snake_case=37			,_snake_case="gelu"			,_snake_case=0.1			,_snake_case=0.1			,_snake_case=5_12			,_snake_case=16			,_snake_case=2			,_snake_case=0.02			,_snake_case=False			,_snake_case=True			,_snake_case="None"			,_snake_case=3			,_snake_case=4			,_snake_case=None			,):
				UpperCAmelCase_     :      Union[str, Any]						=  parent
				UpperCAmelCase_     :      Dict						=  batch_size
				UpperCAmelCase_     :      int						=  seq_length
				UpperCAmelCase_     :      Union[str, Any]						=  is_training
				UpperCAmelCase_     :      Union[str, Any]						=  use_input_mask
				UpperCAmelCase_     :      Optional[Any]						=  use_token_type_ids
				UpperCAmelCase_     :      Dict						=  use_labels
				UpperCAmelCase_     :      int						=  vocab_size
				UpperCAmelCase_     :      int						=  hidden_size
				UpperCAmelCase_     :      Optional[Any]						=  num_hidden_layers
				UpperCAmelCase_     :      Any						=  num_attention_heads
				UpperCAmelCase_     :      Tuple						=  intermediate_size
				UpperCAmelCase_     :      Tuple						=  hidden_act
				UpperCAmelCase_     :      Dict						=  hidden_dropout_prob
				UpperCAmelCase_     :      str						=  attention_probs_dropout_prob
				UpperCAmelCase_     :      int						=  max_position_embeddings
				UpperCAmelCase_     :      str						=  type_vocab_size
				UpperCAmelCase_     :      List[Any]						=  type_sequence_label_size
				UpperCAmelCase_     :      Any						=  initializer_range
				UpperCAmelCase_     :      Optional[Any]						=  num_labels
				UpperCAmelCase_     :      int						=  num_choices
				UpperCAmelCase_     :      str						=  relative_attention
				UpperCAmelCase_     :      List[str]						=  position_biased_input
				UpperCAmelCase_     :      List[str]						=  pos_att_type
				UpperCAmelCase_     :      Optional[int]						=  scope






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      Union[str, Any]						=  ids_tensor([self.batch_size, self.seq_length]			,self.vocab_size     )

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

				UpperCAmelCase_     :      Union[str, Any]						=  None
				if self.use_token_type_ids:
					UpperCAmelCase_     :      Optional[int]						=  ids_tensor([self.batch_size, self.seq_length]			,self.type_vocab_size     )

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

				UpperCAmelCase_     :      Optional[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=_snake_case			,)

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






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case			,_snake_case			,_snake_case			,_snake_case			,_snake_case			,_snake_case     ):
				UpperCAmelCase_     :      List[str]						=  TFDebertaVaModel(config=_snake_case     )
				UpperCAmelCase_     :      Tuple						=  {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

				UpperCAmelCase_     :      Optional[Any]						=  [input_ids, input_mask]
				UpperCAmelCase_     :      Any						=  model(_snake_case     )

				UpperCAmelCase_     :      List[Any]						=  model(_snake_case     )

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






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case			,_snake_case			,_snake_case			,_snake_case			,_snake_case			,_snake_case     ):
				UpperCAmelCase_     :      Optional[int]						=  TFDebertaVaForMaskedLM(config=_snake_case     )
				UpperCAmelCase_     :      Union[str, Any]						=  {
				    "input_ids": input_ids,
				    "attention_mask": input_mask,
				    "token_type_ids": token_type_ids,
				}
				UpperCAmelCase_     :      List[str]						=  model(_snake_case     )
				self.parent.assertEqual(result.logits.shape			,(self.batch_size, self.seq_length, self.vocab_size)     )






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case			,_snake_case			,_snake_case			,_snake_case			,_snake_case			,_snake_case     ):
				UpperCAmelCase_     :      int						=  self.num_labels
				UpperCAmelCase_     :      Tuple						=  TFDebertaVaForSequenceClassification(config=_snake_case     )
				UpperCAmelCase_     :      Any						=  {
				    "input_ids": input_ids,
				    "attention_mask": input_mask,
				    "token_type_ids": token_type_ids,
				}

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






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case			,_snake_case			,_snake_case			,_snake_case			,_snake_case			,_snake_case     ):
				UpperCAmelCase_     :      Dict						=  self.num_labels
				UpperCAmelCase_     :      Tuple						=  TFDebertaVaForTokenClassification(config=_snake_case     )
				UpperCAmelCase_     :      Optional[Any]						=  {
				    "input_ids": input_ids,
				    "attention_mask": input_mask,
				    "token_type_ids": token_type_ids,
				}
				UpperCAmelCase_     :      List[str]						=  model(_snake_case     )
				self.parent.assertEqual(result.logits.shape			,(self.batch_size, self.seq_length, self.num_labels)     )






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case			,_snake_case			,_snake_case			,_snake_case			,_snake_case			,_snake_case     ):
				UpperCAmelCase_     :      List[str]						=  TFDebertaVaForQuestionAnswering(config=_snake_case     )
				UpperCAmelCase_     :      Any						=  {
				    "input_ids": input_ids,
				    "attention_mask": input_mask,
				    "token_type_ids": token_type_ids,
				}

				UpperCAmelCase_     :      List[str]						=  model(_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 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      List[Any]						=  self.prepare_config_and_inputs()
				(
				    (
				    UpperCAmelCase_
				)					,					(
				    UpperCAmelCase_
				)					,					(
				    UpperCAmelCase_
				)					,					(
				    UpperCAmelCase_
				)					,					(
				    UpperCAmelCase_
				)					,					(
				    UpperCAmelCase_
				)					,					(
				    UpperCAmelCase_
				)					,					
				)     :      Optional[int]						=  config_and_inputs
				UpperCAmelCase_     :      Dict						=  {"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):
			__A  :	List[str]        =(
			    (
			        TFDebertaVaModel,
			        TFDebertaVaForMaskedLM,
			        TFDebertaVaForQuestionAnswering,
			        TFDebertaVaForSequenceClassification,
			        TFDebertaVaForTokenClassification,
			    )
			    if is_tf_available()
			    else ()
			)
			__A  :	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 {}
			)

			__A  :	Tuple        =False
			__A  :	List[Any]        =False






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      Optional[int]						=  TFDebertaVaModelTester(self     )
				UpperCAmelCase_     :      Union[str, Any]						=  ConfigTester(self			,config_class=_snake_case			,hidden_size=37     )






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






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      List[Any]						=  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*_snake_case     )






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      str						=  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_masked_lm(*_snake_case     )






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      Tuple						=  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_question_answering(*_snake_case     )






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      int						=  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_sequence_classification(*_snake_case     )






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      Dict						=  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_token_classification(*_snake_case     )






			@slow
			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      Dict						=  TFDebertaVaModel.from_pretrained("kamalkraj/deberta-v2-xlarge"     )
				self.assertIsNotNone(_snake_case     )




@require_tf
class 					_snake_case		(unittest.TestCase):






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






			@slow
			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      List[str]						=  TFDebertaVaModel.from_pretrained("kamalkraj/deberta-v2-xlarge"     )
				UpperCAmelCase_     :      int						=  tf.constant([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]     )
				UpperCAmelCase_     :      List[Any]						=  tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]     )
				UpperCAmelCase_     :      Optional[Any]						=  model(_snake_case			,attention_mask=_snake_case     )[0]

				UpperCAmelCase_     :      List[str]						=  tf.constant(
				    [[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]]     )
				tf.debugging.assert_near(output[:, 1:4, 1:4]			,_snake_case			,atol=1E-4     )










'''simple docstring'''

from collections.abc import Sequence
from queue import Queue






class 		a							:


				def __init__(						self       :   List[str]			,							lowercase_       :   str			,							lowercase_       :   List[Any]			,							lowercase_       :   Dict			,							lowercase_       :   int=None			,							lowercase_       :   List[Any]=None   ):
									snake_case_		     =							start
									snake_case_		     =							end
									snake_case_		     =							val
									snake_case_		     =							(start + end) // 2
									snake_case_		     =							left
									snake_case_		     =							right


				def __repr__(						self       :   Any   ):
									return F"SegmentTreeNode(start={self.start}, end={self.end}, val={self.val})"







class 		a							:


				def __init__(						self       :   Any			,							lowercase_       :   Sequence			,							lowercase_       :   List[str]   ):
									snake_case_		     =							collection
									snake_case_		     =							function
									if self.collection:
														snake_case_		     =							self._build_tree(0			,							len(lowercase_   ) - 1   )


				def     A_					(						self       :   Any			,							lowercase_       :   Dict			,							lowercase_       :   Dict   ):
									self._update_tree(self.root			,							lowercase_			,							lowercase_   )


				def     A_					(						self       :   Optional[Any]			,							lowercase_       :   Tuple			,							lowercase_       :   Optional[int]   ):
									return self._query_range(self.root			,							lowercase_			,							lowercase_   )


				def     A_					(						self       :   str			,							lowercase_       :   int			,							lowercase_       :   int   ):
									if start == end:
														return SegmentTreeNode(lowercase_			,							lowercase_			,							self.collection[start]   )
									snake_case_		     =							(start + end) // 2
									snake_case_		     =							self._build_tree(lowercase_			,							lowercase_   )
									snake_case_		     =							self._build_tree(mid + 1			,							lowercase_   )
									return SegmentTreeNode(lowercase_			,							lowercase_			,							self.fn(left.val			,							right.val   )			,							lowercase_			,							lowercase_   )


				def     A_					(						self       :   Optional[int]			,							lowercase_       :   int			,							lowercase_       :   Optional[Any]			,							lowercase_       :   List[str]   ):
									if node.start == i and node.end == i:
														snake_case_		     =							val
														return
									if i <= node.mid:
														self._update_tree(node.left			,							lowercase_			,							lowercase_   )
									else:
														self._update_tree(node.right			,							lowercase_			,							lowercase_   )
									snake_case_		     =							self.fn(node.left.val			,							node.right.val   )


				def     A_					(						self       :   Tuple			,							lowercase_       :   List[Any]			,							lowercase_       :   List[Any]			,							lowercase_       :   Any   ):
									if node.start == i and node.end == j:
														return node.val

									if i <= node.mid:
														if j <= node.mid:
																			# range in left child tree
																			return self._query_range(node.left			,							lowercase_			,							lowercase_   )
														else:
																			# range in left child tree and right child tree
																			return self.fn(
																			    self._query_range(node.left			,							lowercase_			,							node.mid   )			,							self._query_range(node.right			,							node.mid + 1			,							lowercase_   )			,							)
									else:
														# range in right child tree
														return self._query_range(node.right			,							lowercase_			,							lowercase_   )


				def     A_					(						self       :   Any   ):
									if self.root is not None:
														snake_case_		     =							Queue()
														queue.put(self.root   )
														while not queue.empty():
																			snake_case_		     =							queue.get()
																			yield node

																			if node.left is not None:
																								queue.put(node.left   )

																			if node.right is not None:
																								queue.put(node.right   )


if __name__ == "__main__":
							import operator

							for fn in [operator.add, max, min]:
														print('*' * 50)
														a    :							List[Any]										=    SegmentTree([2, 1, 5, 3, 4], fn)
														for node in arr.traverse():
																					print(node)
														print()

														arr.update(1, 5)
														for node in arr.traverse():
																					print(node)
														print()

														print(arr.query_range(3, 4))  # 7
														print(arr.query_range(2, 2))  # 5
														print(arr.query_range(1, 3))  # 13
														print()

