


from collections.abc import Callable






class    _UpperCamelCase     :

				"""simple docstring"""







				def __init__(    self					, a__ = None )						->					None:
					# Stores actual heap items.
					A             =					[]
					# Stores indexes of each item for supporting updates and deletion.
					A             =					{}
					# Stores current size of heap.
					A             =					0
					# Stores function used to evaluate the score of an item on which basis ordering
					# will be done.
					A             =					key or (lambda a__ : x)

				def   _UpperCAmelCase       (    self					, a__ )						->					int | None:
					return int((i - 1) / 2 ) if i > 0 else None

				def   _UpperCAmelCase       (    self					, a__ )						->					int | None:
					A             =					int(2 * i + 1 )
					return left if 0 < left < self.size else None

				def   _UpperCAmelCase       (    self					, a__ )						->					int | None:
					A             =					int(2 * i + 2 )
					return right if 0 < right < self.size else None

				def   _UpperCAmelCase       (    self					, a__					, a__ )						->					None:
					A       ,      A             =					(
					    self.pos_map[self.arr[j][0]],
					    self.pos_map[self.arr[i][0]],
					)
					# Then swap the items in the list.
					A       ,      A             =					self.arr[j], self.arr[i]

				def   _UpperCAmelCase       (    self					, a__					, a__ )						->					bool:
					return self.arr[i][1] < self.arr[j][1]

				def   _UpperCAmelCase       (    self					, a__ )						->					int:
					A             =					self._left(a__ )
					A             =					self._right(a__ )
					A             =					i

					if left is not None and not self._cmp(a__					, a__ ):
						A             =					left
					if right is not None and not self._cmp(a__					, a__ ):
						A             =					right

					return valid_parent

				def   _UpperCAmelCase       (    self					, a__ )						->					None:
					A             =					self._parent(a__ )
					while parent is not None and not self._cmp(a__					, a__ ):
						self._swap(a__					, a__ )
						A       ,      A             =					parent, self._parent(a__ )

				def   _UpperCAmelCase       (    self					, a__ )						->					None:
					A             =					self._get_valid_parent(a__ )
					while valid_parent != index:
						self._swap(a__					, a__ )
						A       ,      A             =					valid_parent, self._get_valid_parent(a__ )

				def   _UpperCAmelCase       (    self					, a__					, a__ )						->					None:
					if item not in self.pos_map:
						return
					A             =					self.pos_map[item]
					A             =					[item, self.key(a__ )]
					# Make sure heap is right in both up and down direction.
					# Ideally only one of them will make any change.
					self._heapify_up(a__ )
					self._heapify_down(a__ )

				def   _UpperCAmelCase       (    self					, a__ )						->					None:
					if item not in self.pos_map:
						return
					A             =					self.pos_map[item]
					del self.pos_map[item]
					A             =					self.arr[self.size - 1]
					A             =					index
					self.size -= 1
					# Make sure heap is right in both up and down direction. Ideally only one
					# of them will make any change- so no performance loss in calling both.
					if self.size > index:
						self._heapify_up(a__ )
						self._heapify_down(a__ )

				def   _UpperCAmelCase       (    self					, a__					, a__ )						->					None:
					A             =					len(self.arr )
					if arr_len == self.size:
						self.arr.append([item, self.key(a__ )] )
					else:
						A             =					[item, self.key(a__ )]
					A             =					self.size
					self.size += 1
					self._heapify_up(self.size - 1 )

				def   _UpperCAmelCase       (    self )						->					tuple | None:
					return self.arr[0] if self.size else None

				def   _UpperCAmelCase       (    self )						->					tuple | None:
					A             =					self.get_top()
					if top_item_tuple:
						self.delete_item(top_item_tuple[0] )
					return top_item_tuple





def 	_lowerCAmelCase					( )			-> None:





	"""simple docstring"""









if __name__ == "__main__":
				import doctest

				doctest.testmod()







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					):

				"""simple docstring"""







				def __init__(    self					, a__					, a__=2					, a__=56					, a__=True					, a__=True					, a__=True					, a__=True					, a__=99					, a__=32					, a__=2					, a__=2					, a__=7					, a__="gelu_new"					, a__=0.1					, a__=0.1					, a__=512					, a__=16					, a__=2					, a__=0.02					, a__=4					, a__="block_sparse"					, a__=True					, a__=False					, a__=2					, a__=3					, )						->					List[Any]:
					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   _UpperCAmelCase       (    self )						->					List[Any]:
					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=a__					, 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   _UpperCAmelCase       (    self )						->					Optional[int]:
					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     (       __snake_case     ,		unittest.TestCase					):

				"""simple docstring"""







				lowerCAmelCase					=			(
				    (
				        FlaxBigBirdForCausalLM,
				        FlaxBigBirdModel,
				        FlaxBigBirdForPreTraining,
				        FlaxBigBirdForMaskedLM,
				        FlaxBigBirdForMultipleChoice,
				        FlaxBigBirdForQuestionAnswering,
				        FlaxBigBirdForSequenceClassification,
				        FlaxBigBirdForTokenClassification,
				    )
				    if is_flax_available()
				    else ()
				)

				lowerCAmelCase					=			False
				lowerCAmelCase					=			False

				def   _UpperCAmelCase       (    self )						->					Optional[int]:
					A             =					FlaxBigBirdModelTester(self )

				@slow
				# copied from `test_modeling_flax_common` because it takes much longer than other models
				def   _UpperCAmelCase       (    self )						->					Tuple:
					super().test_from_pretrained_save_pretrained()

				@slow
				# copied from `test_modeling_flax_common` because it takes much longer than other models
				def   _UpperCAmelCase       (    self )						->					Dict:
					super().test_from_pretrained_with_no_automatic_init()

				@slow
				# copied from `test_modeling_flax_common` because it takes much longer than other models
				def   _UpperCAmelCase       (    self )						->					str:
					super().test_no_automatic_init()

				@slow
				# copied from `test_modeling_flax_common` because it takes much longer than other models
				def   _UpperCAmelCase       (    self )						->					List[Any]:
					super().test_hidden_states_output()

				@slow
				def   _UpperCAmelCase       (    self )						->					str:
					for model_class_name in self.all_model_classes:
						A             =					model_class_name.from_pretrained("""google/bigbird-roberta-base""" )
						self.assertIsNotNone(a__ )

				def   _UpperCAmelCase       (    self )						->					Optional[int]:
					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   _UpperCAmelCase       (    self )						->					List[Any]:
					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(a__					, a__ )
							A             =					model_class(a__ )

							@jax.jit
							def model_jitted(a__					, a__=None					, **a__ ):
								return model(input_ids=a__					, attention_mask=a__					, **a__ )

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

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

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

				def   _UpperCAmelCase       (    self					, a__					, a__					, a__					, a__=1e-5					, a__="outputs"					, a__=None )						->					Tuple:
					# `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(a__					, a__					, a__					, a__					, a__					, a__ )



