






from __future__ import annotations

import unittest

from transformers import DebertaConfig, 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 (
		    TFDebertaForMaskedLM,
		    TFDebertaForQuestionAnswering,
		    TFDebertaForSequenceClassification,
		    TFDebertaForTokenClassification,
		    TFDebertaModel,
		)





class __lowerCamelCase			:





	"""simple docstring"""






	def __init__(							self	,   UpperCAmelCase	,   UpperCAmelCase=13	,   UpperCAmelCase=7	,   UpperCAmelCase=True	,   UpperCAmelCase=True	,   UpperCAmelCase=True	,   UpperCAmelCase=True	,   UpperCAmelCase=99	,   UpperCAmelCase=32	,   UpperCAmelCase=2	,   UpperCAmelCase=4	,   UpperCAmelCase=37	,   UpperCAmelCase="gelu"	,   UpperCAmelCase=0.1	,   UpperCAmelCase=0.1	,   UpperCAmelCase=512	,   UpperCAmelCase=16	,   UpperCAmelCase=2	,   UpperCAmelCase=0.02	,   UpperCAmelCase=3	,   UpperCAmelCase=4	,   UpperCAmelCase=None	,   ):
							"""simple docstring"""







							_UpperCAmelCase     =      parent
							_UpperCAmelCase     =      13
							_UpperCAmelCase     =      7
							_UpperCAmelCase     =      True
							_UpperCAmelCase     =      True
							_UpperCAmelCase     =      True
							_UpperCAmelCase     =      True
							_UpperCAmelCase     =      99
							_UpperCAmelCase     =      32
							_UpperCAmelCase     =      2
							_UpperCAmelCase     =      4
							_UpperCAmelCase     =      37
							_UpperCAmelCase     =      'gelu'
							_UpperCAmelCase     =      0.1
							_UpperCAmelCase     =      0.1
							_UpperCAmelCase     =      512
							_UpperCAmelCase     =      16
							_UpperCAmelCase     =      False
							_UpperCAmelCase     =      -1
							_UpperCAmelCase     =      True
							_UpperCAmelCase     =      2
							_UpperCAmelCase     =      0.02
							_UpperCAmelCase     =      3
							_UpperCAmelCase     =      4
							_UpperCAmelCase     =      None

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







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

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

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

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

							_UpperCAmelCase     =      DebertaConfig(
							    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	,   max_relative_positions=self.max_relative_positions	,   position_biased_input=self.position_biased_input	,   initializer_range=self.initializer_range	,   return_dict=UpperCAmelCase	,   )

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

	def 	UpperCamelCase   (							self	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase   ):
							"""simple docstring"""







							_UpperCAmelCase     =      TFDebertaModel(config=UpperCAmelCase   )
							_UpperCAmelCase     =      {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}

							_UpperCAmelCase     =      [input_ids, input_mask]
							_UpperCAmelCase     =      model(UpperCAmelCase   )

							_UpperCAmelCase     =      model(UpperCAmelCase   )

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

	def 	UpperCamelCase   (							self	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase   ):
							"""simple docstring"""







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

	def 	UpperCamelCase   (							self	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase   ):
							"""simple docstring"""







							_UpperCAmelCase     =      self.num_labels
							_UpperCAmelCase     =      TFDebertaForSequenceClassification(config=UpperCAmelCase   )
							_UpperCAmelCase     =      {
							    'input_ids': input_ids,
							    'attention_mask': input_mask,
							    'token_type_ids': token_type_ids,
							}

							_UpperCAmelCase     =      model(UpperCAmelCase   )
							self.parent.assertEqual(result.logits.shape	,   (self.batch_size, self.num_labels)   )

	def 	UpperCamelCase   (							self	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase   ):
							"""simple docstring"""







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

	def 	UpperCamelCase   (							self	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase   ):
							"""simple docstring"""







							_UpperCAmelCase     =      TFDebertaForQuestionAnswering(config=UpperCAmelCase   )
							_UpperCAmelCase     =      {
							    'input_ids': input_ids,
							    'attention_mask': input_mask,
							    'token_type_ids': token_type_ids,
							}

							_UpperCAmelCase     =      model(UpperCAmelCase   )
							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   ):
							"""simple docstring"""







							_UpperCAmelCase     =      self.prepare_config_and_inputs()
							(
							    (
							    _UpperCAmelCase
							) ,			(
							    _UpperCAmelCase
							) ,			(
							    _UpperCAmelCase
							) ,			(
							    _UpperCAmelCase
							) ,			(
							    _UpperCAmelCase
							) ,			(
							    _UpperCAmelCase
							) ,			(
							    _UpperCAmelCase
							) ,			
							)     =      config_and_inputs
							_UpperCAmelCase     =      {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
							return config, inputs_dict





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





	"""simple docstring"""






	UpperCamelCase__								=				(
	    (
	        TFDebertaModel,
	        TFDebertaForMaskedLM,
	        TFDebertaForQuestionAnswering,
	        TFDebertaForSequenceClassification,
	        TFDebertaForTokenClassification,
	    )
	    if is_tf_available()
	    else ()
	)
	UpperCamelCase__								=				(
	    {
	        "feature-extraction": TFDebertaModel,
	        "fill-mask": TFDebertaForMaskedLM,
	        "question-answering": TFDebertaForQuestionAnswering,
	        "text-classification": TFDebertaForSequenceClassification,
	        "token-classification": TFDebertaForTokenClassification,
	        "zero-shot": TFDebertaForSequenceClassification,
	    }
	    if is_tf_available()
	    else {}
	)

	UpperCamelCase__								=				False
	UpperCamelCase__								=				False

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      TFDebertaModelTester(self   )
							_UpperCAmelCase     =      ConfigTester(self	,   config_class=UpperCAmelCase	,   hidden_size=37   )

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							self.config_tester.run_common_tests()

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*UpperCAmelCase   )

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*UpperCAmelCase   )

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*UpperCAmelCase   )

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*UpperCAmelCase   )

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*UpperCAmelCase   )

	@slow
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      TFDebertaModel.from_pretrained('kamalkraj/deberta-base'   )
							self.assertIsNotNone(UpperCAmelCase   )





@require_tf
class __lowerCamelCase			(				unittest.TestCase):





	"""simple docstring"""






	@unittest.skip(reason='Model not available yet'   )
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							pass

	@slow
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      TFDebertaModel.from_pretrained('kamalkraj/deberta-base'   )
							_UpperCAmelCase     =      tf.constant([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]   )
							_UpperCAmelCase     =      tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]   )
							_UpperCAmelCase     =      model(UpperCAmelCase	,   attention_mask=UpperCAmelCase   )[0]

							_UpperCAmelCase     =      tf.constant(
							    [
							        [
							            [-0.59_85_58_96, -0.80_55_25_66, -0.8_46_21_35],
							            [1.4_48_40_25, -0.93_48_37_94, -0.80_59_30_85],
							            [0.3_12_27_41, 0.00_31_60_59, -1.4_13_13_77],
							        ]
							    ]   )
							tf.debugging.assert_near(output[:, 1:4, 1:4]	,   UpperCAmelCase	,   atol=1e-4   )








from __future__ import annotations

_a				     =  [2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]





class __lowerCamelCase			:





	"""simple docstring"""






	def __init__(							self	,   UpperCAmelCase   ):
							"""simple docstring"""







							_UpperCAmelCase     =      -1
							_UpperCAmelCase     =      -1
							_UpperCAmelCase     =      [-1] * length
							_UpperCAmelCase     =      None
							_UpperCAmelCase     =      None

	def __repr__(							self   ):
							"""simple docstring"""







							return F"""Node(min_value={self.minn} max_value={self.maxx})"""



def __A   (				__lowerCAmelCase     )->  Node | None:





						"""simple docstring"""



						_UpperCAmelCase     =      Node(len(__lowerCAmelCase     )     )
						_UpperCAmelCase ,			_UpperCAmelCase     =      min(__lowerCAmelCase     ), max(__lowerCAmelCase     )
						# Leaf node case where the node contains only one unique value
						if root.minn == root.maxx:
												return root
						_UpperCAmelCase     =      (root.minn + root.maxx) // 2

						_UpperCAmelCase     =      []
						_UpperCAmelCase     =      []

						for index, num in enumerate(__lowerCAmelCase     ):
												if num <= pivot:
																		left_arr.append(__lowerCAmelCase     )
												else:
																		right_arr.append(__lowerCAmelCase     )
												_UpperCAmelCase     =      len(__lowerCAmelCase     )
						_UpperCAmelCase     =      build_tree(__lowerCAmelCase     )
						_UpperCAmelCase     =      build_tree(__lowerCAmelCase     )
						return root


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )->  int:





						"""simple docstring"""



						if index < 0 or node is None:
												return 0
						# Leaf node cases
						if node.minn == node.maxx:
												return index + 1 if node.minn == num else 0
						_UpperCAmelCase     =      (node.minn + node.maxx) // 2
						if num <= pivot:
												# go the left subtree and map index to the left subtree
												return rank_till_index(node.left			,		__lowerCAmelCase			,		node.map_left[index] - 1     )
						else:
												# go to the right subtree and map index to the right subtree
												return rank_till_index(node.right			,		__lowerCAmelCase			,		index - node.map_left[index]     )


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )->  int:





						"""simple docstring"""



						if start > end:
												return 0
						_UpperCAmelCase     =      rank_till_index(__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )
						_UpperCAmelCase     =      rank_till_index(__lowerCAmelCase			,		__lowerCAmelCase			,		start - 1     )
						return rank_till_end - rank_before_start


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )->  int:





						"""simple docstring"""



						if index > (end - start) or start > end or node is None:
												return -1
						# Leaf node case
						if node.minn == node.maxx:
												return node.minn
						# Number of elements in the left subtree in interval [start, end]
						_UpperCAmelCase     =      node.map_left[end] - (
						    node.map_left[start - 1] if start else 0
						)
						if num_elements_in_left_tree > index:
												return quantile(
												    node.left			,		__lowerCAmelCase			,		(node.map_left[start - 1] if start else 0)			,		node.map_left[end] - 1			,		)
						else:
												return quantile(
												    node.right			,		index - num_elements_in_left_tree			,		start - (node.map_left[start - 1] if start else 0)			,		end - node.map_left[end]			,		)


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )->  int:





						"""simple docstring"""



						if (
						    start > end
						    or node is None
						    or start_num > end_num
						    or node.minn > end_num
						    or node.maxx < start_num
						):
												return 0
						if start_num <= node.minn and node.maxx <= end_num:
												return end - start + 1
						_UpperCAmelCase     =      range_counting(
						    node.left			,		(node.map_left[start - 1] if start else 0)			,		node.map_left[end] - 1			,		__lowerCAmelCase			,		__lowerCAmelCase			,		)
						_UpperCAmelCase     =      range_counting(
						    node.right			,		start - (node.map_left[start - 1] if start else 0)			,		end - node.map_left[end]			,		__lowerCAmelCase			,		__lowerCAmelCase			,		)
						return left + right


if __name__ == "__main__":
		import doctest

		doctest.testmod()
