





'''simple docstring'''


import unittest

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
		import jax

		from transformers.models.big_bird.modeling_flax_big_bird import (
		    FlaxBigBirdForCausalLM,
		    FlaxBigBirdForMaskedLM,
		    FlaxBigBirdForMultipleChoice,
		    FlaxBigBirdForPreTraining,
		    FlaxBigBirdForQuestionAnswering,
		    FlaxBigBirdForSequenceClassification,
		    FlaxBigBirdForTokenClassification,
		    FlaxBigBirdModel,
		)






class       UpperCamelCase__  (		unittest.TestCase     ):







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




				'''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

				a__     = rescale_embeddings
				a__     = attention_type
				a__     = use_bias
				a__     = block_size
				a__     = num_random_blocks







	def 						__a   (				self			:   Dict		):




				'''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__     = BigBirdConfig(
				    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=lowerCamelCase , initializer_range=self.initializer_range , attention_type=self.attention_type , block_size=self.block_size , num_random_blocks=self.num_random_blocks , use_bias=self.use_bias , rescale_embeddings=self.rescale_embeddings , )

				return config, input_ids, token_type_ids, attention_mask







	def 						__a   (				self			:   Dict		):




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






@require_flax
class       UpperCamelCase__  (		__lowerCAmelCase	,unittest.TestCase     ):
	lowerCAmelCase__					:			Any								=						(
	    (
	        FlaxBigBirdForCausalLM,
	        FlaxBigBirdModel,
	        FlaxBigBirdForPreTraining,
	        FlaxBigBirdForMaskedLM,
	        FlaxBigBirdForMultipleChoice,
	        FlaxBigBirdForQuestionAnswering,
	        FlaxBigBirdForSequenceClassification,
	        FlaxBigBirdForTokenClassification,
	    )
	    if is_flax_available()
	    else ()
	)

	lowerCAmelCase__					:			List[str]								=						False
	lowerCAmelCase__					:			Optional[Any]								=						False







	def 						__a   (				self			:   Any		):




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







	@slow
	# copied from `test_modeling_flax_common` because it takes much longer than other models
	def 						__a   (				self			:   Dict		):




				'''simple docstring'''
				super().test_from_pretrained_save_pretrained()







	@slow
	# copied from `test_modeling_flax_common` because it takes much longer than other models
	def 						__a   (				self			:   Any		):




				'''simple docstring'''
				super().test_from_pretrained_with_no_automatic_init()







	@slow
	# copied from `test_modeling_flax_common` because it takes much longer than other models
	def 						__a   (				self			:   Any		):




				'''simple docstring'''
				super().test_no_automatic_init()







	@slow
	# copied from `test_modeling_flax_common` because it takes much longer than other models
	def 						__a   (				self			:   Optional[Any]		):




				'''simple docstring'''
				super().test_hidden_states_output()







	@slow
	def 						__a   (				self			:   int		):




				'''simple docstring'''
				for model_class_name in self.all_model_classes:
							a__     = model_class_name.from_pretrained("google/bigbird-roberta-base"		)
							self.assertIsNotNone(lowerCamelCase		)







	def 						__a   (				self			:   List[Any]		):




				'''simple docstring'''
				if self.test_attn_probs:
							super().test_attention_outputs()







	@slow
	# copied from `test_modeling_flax_common` because it takes much longer than other models
	def 						__a   (				self			:   Optional[int]		):




				'''simple docstring'''
				a__   ,				a__     = self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							with self.subTest(model_class.__name__		):
										a__     = self._prepare_for_class(lowerCamelCase , lowerCamelCase		)
										a__     = model_class(lowerCamelCase		)

										@jax.jit
										def model_jitted(lowerCamelCase			:   Optional[Any] , lowerCamelCase			:   List[Any]=None , **lowerCamelCase			:   str		):
													return model(input_ids=lowerCamelCase , attention_mask=lowerCamelCase , **lowerCamelCase		)

										with self.subTest("JIT Enabled"		):
													a__     = model_jitted(**lowerCamelCase		).to_tuple()

										with self.subTest("JIT Disabled"		):
													with jax.disable_jit():
																a__     = model_jitted(**lowerCamelCase		).to_tuple()

										self.assertEqual(len(lowerCamelCase		) , len(lowerCamelCase		)		)
										for jitted_output, output in zip(lowerCamelCase , lowerCamelCase		):
													self.assertEqual(jitted_output.shape , output.shape		)







	def 						__a   (				self			:   Optional[Any] , lowerCamelCase			:   Dict , lowerCamelCase			:   List[str] , lowerCamelCase			:   Optional[Any] , lowerCamelCase			:   List[str]=1e-5 , lowerCamelCase			:   str="outputs" , lowerCamelCase			:   Any=None		):




				'''simple docstring'''
				# `bigbird_block_sparse_attention` in `FlaxBigBird` returns `attention_probs = None`, while in PyTorch version,
				# an effort was done to return `attention_probs` (yet to be verified).
				if name.startswith("outputs.attentions"		):
							return
				else:
							super().check_pt_flax_outputs(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase		)










'''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



