


'''simple docstring'''
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 		UpperCamelCase_		:





						"""simple docstring"""




						def __init__( self				:					Dict	,				_lowerCamelCase				:					str	,				_lowerCamelCase				:					Dict=13	,				_lowerCamelCase				:					Union[str, Any]=7	,				_lowerCamelCase				:					Union[str, Any]=True	,				_lowerCamelCase				:					Optional[Any]=True	,				_lowerCamelCase				:					Tuple=True	,				_lowerCamelCase				:					Any=True	,				_lowerCamelCase				:					int=99	,				_lowerCamelCase				:					Any=32	,				_lowerCamelCase				:					Any=2	,				_lowerCamelCase				:					str=4	,				_lowerCamelCase				:					str=37	,				_lowerCamelCase				:					Optional[int]="gelu"	,				_lowerCamelCase				:					Optional[Any]=0.1	,				_lowerCamelCase				:					Dict=0.1	,				_lowerCamelCase				:					int=5_12	,				_lowerCamelCase				:					Union[str, Any]=16	,				_lowerCamelCase				:					int=2	,				_lowerCamelCase				:					List[Any]=0.02	,				_lowerCamelCase				:					str=3	,				_lowerCamelCase				:					Tuple=4	,				_lowerCamelCase				:					int=None	,				)							->					Optional[int]:
										__magic_name__	  = parent
										__magic_name__	  = 13
										__magic_name__	  = 7
										__magic_name__	  = True
										__magic_name__	  = True
										__magic_name__	  = True
										__magic_name__	  = True
										__magic_name__	  = 99
										__magic_name__	  = 32
										__magic_name__	  = 2
										__magic_name__	  = 4
										__magic_name__	  = 37
										__magic_name__	  = "gelu"
										__magic_name__	  = 0.1
										__magic_name__	  = 0.1
										__magic_name__	  = 5_12
										__magic_name__	  = 16
										__magic_name__	  = False
										__magic_name__	  = -1
										__magic_name__	  = True
										__magic_name__	  = 2
										__magic_name__	  = 0.02
										__magic_name__	  = 3
										__magic_name__	  = 4
										__magic_name__	  = None
						def 					__A					( self				:					Any  )							->					Union[str, Any]:
										__magic_name__	  = ids_tensor([self.batch_size, self.seq_length]	,				self.vocab_size  )

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

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

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

										__magic_name__	  = 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=_lowerCamelCase	,				)

										return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
						def 					__A					( self				:					Optional[Any]	,				_lowerCamelCase				:					Optional[Any]	,				_lowerCamelCase				:					Optional[int]	,				_lowerCamelCase				:					Optional[int]	,				_lowerCamelCase				:					Union[str, Any]	,				_lowerCamelCase				:					List[Any]	,				_lowerCamelCase				:					Tuple	,				_lowerCamelCase				:					Union[str, Any]  )							->					Optional[int]:
										__magic_name__	  = TFDebertaModel(config=_lowerCamelCase  )
										__magic_name__	  = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

										__magic_name__	  = [input_ids, input_mask]
										__magic_name__	  = model(_lowerCamelCase  )

										__magic_name__	  = model(_lowerCamelCase  )

										self.parent.assertEqual(result.last_hidden_state.shape	,				(self.batch_size, self.seq_length, self.hidden_size)  )
						def 					__A					( self				:					Optional[int]	,				_lowerCamelCase				:					Optional[int]	,				_lowerCamelCase				:					int	,				_lowerCamelCase				:					str	,				_lowerCamelCase				:					Union[str, Any]	,				_lowerCamelCase				:					Union[str, Any]	,				_lowerCamelCase				:					Union[str, Any]	,				_lowerCamelCase				:					Tuple  )							->					Dict:
										__magic_name__	  = TFDebertaForMaskedLM(config=_lowerCamelCase  )
										__magic_name__	  = {
										    "input_ids": input_ids,
										    "attention_mask": input_mask,
										    "token_type_ids": token_type_ids,
										}
										__magic_name__	  = model(_lowerCamelCase  )
										self.parent.assertEqual(result.logits.shape	,				(self.batch_size, self.seq_length, self.vocab_size)  )
						def 					__A					( self				:					List[str]	,				_lowerCamelCase				:					Tuple	,				_lowerCamelCase				:					Dict	,				_lowerCamelCase				:					Optional[int]	,				_lowerCamelCase				:					List[Any]	,				_lowerCamelCase				:					List[Any]	,				_lowerCamelCase				:					str	,				_lowerCamelCase				:					Tuple  )							->					str:
										__magic_name__	  = self.num_labels
										__magic_name__	  = TFDebertaForSequenceClassification(config=_lowerCamelCase  )
										__magic_name__	  = {
										    "input_ids": input_ids,
										    "attention_mask": input_mask,
										    "token_type_ids": token_type_ids,
										}

										__magic_name__	  = model(_lowerCamelCase  )
										self.parent.assertEqual(result.logits.shape	,				(self.batch_size, self.num_labels)  )
						def 					__A					( self				:					List[str]	,				_lowerCamelCase				:					Optional[int]	,				_lowerCamelCase				:					Any	,				_lowerCamelCase				:					List[str]	,				_lowerCamelCase				:					Optional[int]	,				_lowerCamelCase				:					List[str]	,				_lowerCamelCase				:					int	,				_lowerCamelCase				:					List[str]  )							->					Tuple:
										__magic_name__	  = self.num_labels
										__magic_name__	  = TFDebertaForTokenClassification(config=_lowerCamelCase  )
										__magic_name__	  = {
										    "input_ids": input_ids,
										    "attention_mask": input_mask,
										    "token_type_ids": token_type_ids,
										}
										__magic_name__	  = model(_lowerCamelCase  )
										self.parent.assertEqual(result.logits.shape	,				(self.batch_size, self.seq_length, self.num_labels)  )
						def 					__A					( self				:					str	,				_lowerCamelCase				:					int	,				_lowerCamelCase				:					Optional[int]	,				_lowerCamelCase				:					Union[str, Any]	,				_lowerCamelCase				:					List[str]	,				_lowerCamelCase				:					List[Any]	,				_lowerCamelCase				:					List[str]	,				_lowerCamelCase				:					List[str]  )							->					Tuple:
										__magic_name__	  = TFDebertaForQuestionAnswering(config=_lowerCamelCase  )
										__magic_name__	  = {
										    "input_ids": input_ids,
										    "attention_mask": input_mask,
										    "token_type_ids": token_type_ids,
										}

										__magic_name__	  = model(_lowerCamelCase  )
										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				:					Dict  )							->					Dict:
										__magic_name__	  = self.prepare_config_and_inputs()
										(
										    (
										    __magic_name__
										)  ,      (
										    __magic_name__
										)  ,      (
										    __magic_name__
										)  ,      (
										    __magic_name__
										)  ,      (
										    __magic_name__
										)  ,      (
										    __magic_name__
										)  ,      (
										    __magic_name__
										)  ,      
										)	  = config_and_inputs
										__magic_name__	  = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
										return config, inputs_dict






@require_tf
class 		UpperCamelCase_		(					A   ,		A   ,		unittest.TestCase   ):





						"""simple docstring"""




						UpperCAmelCase__	:       Tuple         =     (
						    (
						        TFDebertaModel,
						        TFDebertaForMaskedLM,
						        TFDebertaForQuestionAnswering,
						        TFDebertaForSequenceClassification,
						        TFDebertaForTokenClassification,
						    )
						    if is_tf_available()
						    else ()
						)
						UpperCAmelCase__	:       Optional[Any]         =     (
						    {
						        '''feature-extraction''': TFDebertaModel,
						        '''fill-mask''': TFDebertaForMaskedLM,
						        '''question-answering''': TFDebertaForQuestionAnswering,
						        '''text-classification''': TFDebertaForSequenceClassification,
						        '''token-classification''': TFDebertaForTokenClassification,
						        '''zero-shot''': TFDebertaForSequenceClassification,
						    }
						    if is_tf_available()
						    else {}
						)

						UpperCAmelCase__	:       List[str]         =     False
						UpperCAmelCase__	:       List[str]         =     False
						def 					__A					( self				:					Optional[Any]  )							->					str:
										__magic_name__	  = TFDebertaModelTester(self  )
										__magic_name__	  = ConfigTester(self	,				config_class=_lowerCamelCase	,				hidden_size=37  )
						def 					__A					( self				:					int  )							->					Optional[Any]:
										self.config_tester.run_common_tests()
						def 					__A					( self				:					List[str]  )							->					Optional[Any]:
										__magic_name__	  = self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*_lowerCamelCase  )
						def 					__A					( self				:					List[str]  )							->					Dict:
										__magic_name__	  = self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_lm(*_lowerCamelCase  )
						def 					__A					( self				:					Tuple  )							->					Union[str, Any]:
										__magic_name__	  = self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(*_lowerCamelCase  )
						def 					__A					( self				:					List[Any]  )							->					Union[str, Any]:
										__magic_name__	  = self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(*_lowerCamelCase  )
						def 					__A					( self				:					Union[str, Any]  )							->					str:
										__magic_name__	  = self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*_lowerCamelCase  )

						@slow
						def 					__A					( self				:					Optional[int]  )							->					Any:
										__magic_name__	  = TFDebertaModel.from_pretrained("kamalkraj/deberta-base"  )
										self.assertIsNotNone(_lowerCamelCase  )






@require_tf
class 		UpperCamelCase_		(					unittest.TestCase   ):





						"""simple docstring"""




						@unittest.skip(reason="Model not available yet"  )
						def 					__A					( self				:					str  )							->					str:
										pass

						@slow
						def 					__A					( self				:					str  )							->					Dict:
										__magic_name__	  = TFDebertaModel.from_pretrained("kamalkraj/deberta-base"  )
										__magic_name__	  = tf.constant([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]  )
										__magic_name__	  = tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]  )
										__magic_name__	  = model(_lowerCamelCase	,				attention_mask=_lowerCamelCase  )[0]

										__magic_name__	  = tf.constant(
										    [
										        [
										            [-0.59_855_896, -0.80_552_566, -0.8_462_135],
										            [1.4_484_025, -0.93_483_794, -0.80_593_085],
										            [0.3_122_741, 0.00_316_059, -1.4_131_377],
										        ]
										    ]  )
										tf.debugging.assert_near(output[:, 1:4, 1:4]	,				_lowerCamelCase	,				atol=1e-4  )







'''simple docstring'''
from __future__ import annotations




def      __snake_case       (       lowerCamelCase_ :				list[list[int]]	,  lowerCamelCase_ :				list[list[int]]						):
				'''simple docstring'''




				assert len(lowerCamelCase_						) == len(lowerCamelCase_						)

				__magic_name__	  = len(lowerCamelCase_						)
				__magic_name__	  = list(range(lowerCamelCase_						)						)
				__magic_name__	  = [-1] * n  # who the donor has donated to
				__magic_name__	  = [-1] * n  # who the recipient has received from
				__magic_name__	  = [0] * n

				while unmatched_donors:
								__magic_name__	  = unmatched_donors[0]
								__magic_name__	  = donor_pref[donor]
								__magic_name__	  = donor_preference[num_donations[donor]]
								num_donations[donor] += 1
								__magic_name__	  = recipient_pref[recipient]
								__magic_name__	  = rec_record[recipient]

								if prev_donor != -1:
												if rec_preference.index(lowerCamelCase_						) > rec_preference.index(lowerCamelCase_						):
																__magic_name__	  = donor
																__magic_name__	  = recipient
																unmatched_donors.append(lowerCamelCase_						)
																unmatched_donors.remove(lowerCamelCase_						)
								else:
												__magic_name__	  = donor
												__magic_name__	  = recipient
												unmatched_donors.remove(lowerCamelCase_						)
				return donor_record



