
'''simple docstring'''


import unittest

import numpy as np

from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
		from transformers.models.roberta.modeling_flax_roberta import (
		    FlaxRobertaForCausalLM,
		    FlaxRobertaForMaskedLM,
		    FlaxRobertaForMultipleChoice,
		    FlaxRobertaForQuestionAnswering,
		    FlaxRobertaForSequenceClassification,
		    FlaxRobertaForTokenClassification,
		    FlaxRobertaModel,
		)






class       UpperCamelCase__  (		unittest.TestCase     ):







	def __init__(				self			:   int , lowerCamelCase			:   Tuple , lowerCamelCase			:   Tuple=1_3 , lowerCamelCase			:   List[Any]=7 , lowerCamelCase			:   Dict=True , lowerCamelCase			:   List[str]=True , lowerCamelCase			:   Union[str, Any]=True , lowerCamelCase			:   Dict=True , lowerCamelCase			:   str=9_9 , lowerCamelCase			:   List[Any]=3_2 , lowerCamelCase			:   Optional[Any]=5 , lowerCamelCase			:   List[Any]=4 , lowerCamelCase			:   int=3_7 , lowerCamelCase			:   Tuple="gelu" , lowerCamelCase			:   Union[str, Any]=0.1 , lowerCamelCase			:   List[str]=0.1 , lowerCamelCase			:   List[str]=5_1_2 , lowerCamelCase			:   Any=1_6 , lowerCamelCase			:   Optional[int]=2 , lowerCamelCase			:   Union[str, Any]=0.02 , lowerCamelCase			:   Optional[int]=4 , ):




				'''simple docstring'''
				a__     = parent
				a__     = batch_size
				a__     = seq_length
				a__     = is_training
				a__     = use_attention_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_choices







	def 						__a   (				self			:   Union[str, Any]		):




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

				a__     = None
				if self.use_attention_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__     = RobertaConfig(
				    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 , is_decoder=_lowercase , initializer_range=self.initializer_range , )

				return config, input_ids, token_type_ids, attention_mask







	def 						__a   (				self			:   Tuple		):




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







	def 						__a   (				self			:   List[str]		):




				'''simple docstring'''
				a__     = self.prepare_config_and_inputs()
				a__     = config_and_inputs

				a__     = True
				a__     = floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
				a__     = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2		)

				return (
				    config,
				    input_ids,
				    token_type_ids,
				    encoder_hidden_states,
				    encoder_attention_mask,
				)






@require_flax
class       UpperCamelCase__  (		__snake_case	,unittest.TestCase     ):
	lowerCAmelCase__					:			Dict								=						True

	lowerCAmelCase__					:			List[str]								=						(
	    (
	        FlaxRobertaModel,
	        FlaxRobertaForCausalLM,
	        FlaxRobertaForMaskedLM,
	        FlaxRobertaForSequenceClassification,
	        FlaxRobertaForTokenClassification,
	        FlaxRobertaForMultipleChoice,
	        FlaxRobertaForQuestionAnswering,
	    )
	    if is_flax_available()
	    else ()
	)







	def 						__a   (				self			:   List[Any]		):




				'''simple docstring'''
				a__     = FlaxRobertaModelTester(self		)







	@slow
	def 						__a   (				self			:   List[str]		):




				'''simple docstring'''

				for model_class_name in self.all_model_classes:
							a__     = model_class_name.from_pretrained("roberta-base" , from_pt=_lowercase		)
							a__     = model(np.ones((1, 1)		)		)
							self.assertIsNotNone(_lowercase		)










'''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



