
'''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       UpperCamelCase__  :







	def __init__(				self			:   List[str] , lowerCamelCase			:   Any , lowerCamelCase			:   Tuple=1_3 , lowerCamelCase			:   Union[str, Any]=7 , lowerCamelCase			:   Union[str, Any]=True , lowerCamelCase			:   Tuple=True , lowerCamelCase			:   Any=True , lowerCamelCase			:   Dict=True , lowerCamelCase			:   Optional[int]=9_9 , lowerCamelCase			:   int=3_2 , lowerCamelCase			:   List[Any]=2 , lowerCamelCase			:   Optional[Any]=4 , lowerCamelCase			:   Optional[Any]=3_7 , lowerCamelCase			:   int="gelu" , lowerCamelCase			:   Any=0.1 , lowerCamelCase			:   str=0.1 , lowerCamelCase			:   Tuple=5_1_2 , lowerCamelCase			:   List[Any]=1_6 , lowerCamelCase			:   Union[str, Any]=2 , lowerCamelCase			:   Union[str, Any]=0.02 , lowerCamelCase			:   int=False , lowerCamelCase			:   Any=True , lowerCamelCase			:   int="None" , lowerCamelCase			:   Optional[Any]=3 , lowerCamelCase			:   str=4 , lowerCamelCase			:   Tuple=None , ):




				'''simple docstring'''
				a__     = parent
				a__     = batch_size
				a__     = seq_length
				a__     = is_training
				a__     = use_input_mask
				a__     = use_token_type_ids
				a__     = use_labels
				a__     = vocab_size
				a__     = hidden_size
				a__     = num_hidden_layers
				a__     = num_attention_heads
				a__     = intermediate_size
				a__     = hidden_act
				a__     = hidden_dropout_prob
				a__     = attention_probs_dropout_prob
				a__     = max_position_embeddings
				a__     = type_vocab_size
				a__     = type_sequence_label_size
				a__     = initializer_range
				a__     = num_labels
				a__     = num_choices
				a__     = relative_attention
				a__     = position_biased_input
				a__     = pos_att_type
				a__     = scope







	def 						__a   (				self			:   Tuple		):




				'''simple docstring'''
				a__     = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size		)

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

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

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

				a__     = 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=__UpperCamelCase , )

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







	def 						__a   (				self			:   Optional[Any] , lowerCamelCase			:   Dict , lowerCamelCase			:   List[str] , lowerCamelCase			:   str , lowerCamelCase			:   List[str] , lowerCamelCase			:   Optional[Any] , lowerCamelCase			:   int , lowerCamelCase			:   Dict		):




				'''simple docstring'''
				a__     = TFDebertaVaModel(config=__UpperCamelCase		)
				a__     = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

				a__     = [input_ids, input_mask]
				a__     = model(__UpperCamelCase		)

				a__     = model(__UpperCamelCase		)

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







	def 						__a   (				self			:   Optional[Any] , lowerCamelCase			:   int , lowerCamelCase			:   Tuple , lowerCamelCase			:   List[str] , lowerCamelCase			:   List[Any] , lowerCamelCase			:   Optional[Any] , lowerCamelCase			:   int , lowerCamelCase			:   str		):




				'''simple docstring'''
				a__     = TFDebertaVaForMaskedLM(config=__UpperCamelCase		)
				a__     = {
				    "input_ids": input_ids,
				    "attention_mask": input_mask,
				    "token_type_ids": token_type_ids,
				}
				a__     = model(__UpperCamelCase		)
				self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size)		)







	def 						__a   (				self			:   str , lowerCamelCase			:   Union[str, Any] , lowerCamelCase			:   List[Any] , lowerCamelCase			:   Tuple , lowerCamelCase			:   Optional[Any] , lowerCamelCase			:   Tuple , lowerCamelCase			:   str , lowerCamelCase			:   Optional[Any]		):




				'''simple docstring'''
				a__     = self.num_labels
				a__     = TFDebertaVaForSequenceClassification(config=__UpperCamelCase		)
				a__     = {
				    "input_ids": input_ids,
				    "attention_mask": input_mask,
				    "token_type_ids": token_type_ids,
				}

				a__     = model(__UpperCamelCase		)
				self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels)		)







	def 						__a   (				self			:   Tuple , lowerCamelCase			:   Optional[int] , lowerCamelCase			:   Optional[int] , lowerCamelCase			:   List[str] , lowerCamelCase			:   str , lowerCamelCase			:   Any , lowerCamelCase			:   List[Any] , lowerCamelCase			:   List[Any]		):




				'''simple docstring'''
				a__     = self.num_labels
				a__     = TFDebertaVaForTokenClassification(config=__UpperCamelCase		)
				a__     = {
				    "input_ids": input_ids,
				    "attention_mask": input_mask,
				    "token_type_ids": token_type_ids,
				}
				a__     = model(__UpperCamelCase		)
				self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels)		)







	def 						__a   (				self			:   Optional[Any] , lowerCamelCase			:   Union[str, Any] , lowerCamelCase			:   int , lowerCamelCase			:   str , lowerCamelCase			:   Dict , lowerCamelCase			:   Optional[int] , lowerCamelCase			:   Dict , lowerCamelCase			:   Optional[int]		):




				'''simple docstring'''
				a__     = TFDebertaVaForQuestionAnswering(config=__UpperCamelCase		)
				a__     = {
				    "input_ids": input_ids,
				    "attention_mask": input_mask,
				    "token_type_ids": token_type_ids,
				}

				a__     = 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 						__a   (				self			:   str		):




				'''simple docstring'''
				a__     = self.prepare_config_and_inputs()
				(
				    (
				    a__
				)   ,				(
				    a__
				)   ,				(
				    a__
				)   ,				(
				    a__
				)   ,				(
				    a__
				)   ,				(
				    a__
				)   ,				(
				    a__
				)   ,				
				)     = config_and_inputs
				a__     = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
				return config, inputs_dict






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

	lowerCAmelCase__					:			int								=						False
	lowerCAmelCase__					:			Tuple								=						False







	def 						__a   (				self			:   Optional[Any]		):




				'''simple docstring'''
				a__     = TFDebertaVaModelTester(self		)
				a__     = ConfigTester(self , config_class=__UpperCamelCase , hidden_size=3_7		)







	def 						__a   (				self			:   Optional[Any]		):




				'''simple docstring'''
				self.config_tester.run_common_tests()







	def 						__a   (				self			:   List[str]		):




				'''simple docstring'''
				a__     = self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*__UpperCamelCase		)







	def 						__a   (				self			:   List[Any]		):




				'''simple docstring'''
				a__     = self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_masked_lm(*__UpperCamelCase		)







	def 						__a   (				self			:   Tuple		):




				'''simple docstring'''
				a__     = self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_question_answering(*__UpperCamelCase		)







	def 						__a   (				self			:   Optional[Any]		):




				'''simple docstring'''
				a__     = self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_sequence_classification(*__UpperCamelCase		)







	def 						__a   (				self			:   List[Any]		):




				'''simple docstring'''
				a__     = self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_token_classification(*__UpperCamelCase		)







	@slow
	def 						__a   (				self			:   Dict		):




				'''simple docstring'''
				a__     = TFDebertaVaModel.from_pretrained("kamalkraj/deberta-v2-xlarge"		)
				self.assertIsNotNone(__UpperCamelCase		)






@require_tf
class       UpperCamelCase__  (		unittest.TestCase     ):







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




				'''simple docstring'''
				pass







	@slow
	def 						__a   (				self			:   Optional[int]		):




				'''simple docstring'''
				a__     = TFDebertaVaModel.from_pretrained("kamalkraj/deberta-v2-xlarge"		)
				a__     = tf.constant([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]		)
				a__     = tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]		)
				a__     = model(__UpperCamelCase , attention_mask=__UpperCamelCase		)[0]

				a__     = 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] , __UpperCamelCase , atol=1e-4		)










'''simple docstring'''


def    _lowerCamelCase  (__lowerCamelCase  :     list[list[float]]     )   ->    list[list[float]]:
			a__     = []
			for data in source_data:
						for i, el in enumerate(__lowerCamelCase     ):
									if len(__lowerCamelCase     ) < i + 1:
												data_lists.append([]     )
									data_lists[i].append(float(__lowerCamelCase     )     )
			return data_lists







def    _lowerCamelCase  (__lowerCamelCase  :     list[list[float]]					,  __lowerCamelCase  :     list[int]     )   ->    list[list[float]]:
			a__     = []
			for dlist, weight in zip(__lowerCamelCase					,  __lowerCamelCase     ):
						a__     = min(__lowerCamelCase     )
						a__     = max(__lowerCamelCase     )

						a__     = []
						# for weight 0 score is 1 - actual score
						if weight == 0:
									for item in dlist:
												try:
															score.append(1 - ((item - mind) / (maxd - mind))     )
												except ZeroDivisionError:
															score.append(1     )

						elif weight == 1:
									for item in dlist:
												try:
															score.append((item - mind) / (maxd - mind)     )
												except ZeroDivisionError:
															score.append(0     )

        # weight not 0 or 1
						else:
									a__     = f'''Invalid weight of {weight:f} provided'''
									raise ValueError(__lowerCamelCase     )

						score_lists.append(__lowerCamelCase     )

			return score_lists







def    _lowerCamelCase  (__lowerCamelCase  :     list[list[float]]     )   ->    list[float]:
			a__     = [0 for i in range(len(score_lists[0]     )     )]

			for slist in score_lists:
						for j, ele in enumerate(__lowerCamelCase     ):
									a__     = final_scores[j] + ele

			return final_scores







def    _lowerCamelCase  (__lowerCamelCase  :     list[list[float]]					,  __lowerCamelCase  :     list[int]     )   ->    list[list[float]]:
			a__     = get_data(__lowerCamelCase     )
			a__     = calculate_each_score(__lowerCamelCase					,  __lowerCamelCase     )
			a__     = generate_final_scores(__lowerCamelCase     )

			# append scores to source data
			for i, ele in enumerate(__lowerCamelCase     ):
						source_data[i].append(__lowerCamelCase     )

			return source_data



