



import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import is_torch_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask


if is_torch_available():
		import torch

		from transformers import BlipTextModel
		from transformers.models.blip.modeling_blip import BLIP_PRETRAINED_MODEL_ARCHIVE_LIST




class 				lowerCAmelCase__	:







					def __init__(							self   :  int      ,		_A   :  Union[str, Any]      ,		_A   :  List[str]=12      ,		_A   :  Dict=7      ,		_A   :  Any=True      ,		_A   :  Optional[Any]=True      ,		_A   :  Optional[Any]=True      ,		_A   :  Any=99      ,		_A   :  Optional[Any]=32      ,		_A   :  str=32      ,		_A   :  Optional[int]=5      ,		_A   :  Any=4      ,		_A   :  str=37      ,		_A   :  Optional[int]=0.1      ,		_A   :  Optional[int]=0.1      ,		_A   :  Dict=512      ,		_A   :  Any=0.02      ,		_A   :  Any=0      ,		_A   :  Any=None      ,		):
											A__						:				Optional[int]         =			parent
											A__						:				List[Any]         =			batch_size
											A__						:				List[Any]         =			seq_length
											A__						:				Tuple         =			is_training
											A__						:				Any         =			use_input_mask
											A__						:				List[Any]         =			use_labels
											A__						:				Optional[Any]         =			vocab_size
											A__						:				Any         =			hidden_size
											A__						:				Tuple         =			projection_dim
											A__						:				List[Any]         =			num_hidden_layers
											A__						:				int         =			num_attention_heads
											A__						:				Optional[Any]         =			intermediate_size
											A__						:				str         =			dropout
											A__						:				Optional[int]         =			attention_dropout
											A__						:				Optional[int]         =			max_position_embeddings
											A__						:				Tuple         =			initializer_range
											A__						:				Tuple         =			scope
											A__						:				Union[str, Any]         =			bos_token_id







					def 			_lowercase      (							self   :  List[str]):
											A__						:				Optional[Any]         =			ids_tensor([self.batch_size, self.seq_length]      ,		self.vocab_size)

											A__						:				Optional[Any]         =			None
											if self.use_input_mask:
																	A__						:				Optional[int]         =			random_attention_mask([self.batch_size, self.seq_length])

											if input_mask is not None:
																	A__     ,							A__						:				int         =			input_mask.shape
																	A__						:				Tuple         =			np.random.randint(1      ,		seq_length - 1      ,		size=(batch_size,))
																	for batch_idx, start_index in enumerate(_A):
																							A__						:				List[str]         =			1
																							A__						:				int         =			0

											A__						:				Optional[Any]         =			self.get_config()

											return config, input_ids, input_mask







					def 			_lowercase      (							self   :  str):
											return BlipTextConfig(
											    vocab_size=self.vocab_size      ,		hidden_size=self.hidden_size      ,		projection_dim=self.projection_dim      ,		num_hidden_layers=self.num_hidden_layers      ,		num_attention_heads=self.num_attention_heads      ,		intermediate_size=self.intermediate_size      ,		dropout=self.dropout      ,		attention_dropout=self.attention_dropout      ,		max_position_embeddings=self.max_position_embeddings      ,		initializer_range=self.initializer_range      ,		bos_token_id=self.bos_token_id      ,		)







					def 			_lowercase      (							self   :  Tuple      ,		_A   :  int      ,		_A   :  Optional[Any]      ,		_A   :  str):
											A__						:				Union[str, Any]         =			BlipTextModel(config=_A)
											model.to(_A)
											model.eval()
											with torch.no_grad():
																	A__						:				List[Any]         =			model(_A      ,		attention_mask=_A)
																	A__						:				List[Any]         =			model(_A)
											self.parent.assertEqual(result.last_hidden_state.shape      ,		(self.batch_size, self.seq_length, self.hidden_size))
											self.parent.assertEqual(result.pooler_output.shape      ,		(self.batch_size, self.hidden_size))







					def 			_lowercase      (							self   :  List[str]):
											A__						:				Any         =			self.prepare_config_and_inputs()
											A__     ,							A__     ,							A__						:				Union[str, Any]         =			config_and_inputs
											A__						:				List[Any]         =			{"input_ids": input_ids, "attention_mask": input_mask}
											return config, inputs_dict




@require_torch
class 				lowerCAmelCase__	(						UpperCamelCase       ,				unittest.TestCase    ):
					__A  :     Tuple     =    (BlipTextModel,) if is_torch_available() else ()
					__A  :     Tuple     =    False
					__A  :     Optional[int]     =    False
					__A  :     List[str]     =    False







					def 			_lowercase      (							self   :  Union[str, Any]):
											A__						:				Optional[int]         =			BlipTextModelTester(self)
											A__						:				int         =			ConfigTester(self      ,		config_class=_A      ,		hidden_size=37)







					def 			_lowercase      (							self   :  int):
											self.config_tester.run_common_tests()







					def 			_lowercase      (							self   :  Tuple):
											A__						:				Any         =			self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*_A)







					def 			_lowercase      (							self   :  str):
											pass







					def 			_lowercase      (							self   :  str):
											pass







					@unittest.skip(reason="Blip does not use inputs_embeds")
					def 			_lowercase      (							self   :  Optional[Any]):
											pass







					@unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING")
					def 			_lowercase      (							self   :  Any):
											pass







					@unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING")
					def 			_lowercase      (							self   :  Optional[int]):
											pass







					@slow
					def 			_lowercase      (							self   :  Tuple):
											for model_name in BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	A__						:				Optional[Any]         =			BlipTextModel.from_pretrained(_A)
																	self.assertIsNotNone(_A)







					def 			_lowercase      (							self   :  Optional[int]):
											super().test_pt_tf_model_equivalence(allow_missing_keys=_A)




class 				lowerCAmelCase__	:







					def __init__(							self   :  Union[str, Any]):
											A__						:				Optional[int]         =			[0]
											A__						:				Any         =			0







					def 			_lowercase      (							self   :  Optional[int]      ,		_A   :  int):
											A__						:				Optional[Any]         =			self.__heap[i]
											while i // 2 > 0:
																	if self.__heap[i] > self.__heap[i // 2]:
																							A__						:				Union[str, Any]         =			self.__heap[i // 2]
																							A__						:				List[str]         =			temporary
																	i //= 2







					def 			_lowercase      (							self   :  Union[str, Any]      ,		_A   :  int):
											self.__heap.append(_A)
											self.__size += 1
											self.__swap_up(self.__size)







					def 			_lowercase      (							self   :  Any      ,		_A   :  int):
											while self.__size >= 2 * i:
																	if 2 * i + 1 > self.__size:
																							A__						:				str         =			2 * i
																	else:
																							if self.__heap[2 * i] > self.__heap[2 * i + 1]:
																													A__						:				Tuple         =			2 * i
																							else:
																													A__						:				Tuple         =			2 * i + 1
																	A__						:				Any         =			self.__heap[i]
																	if self.__heap[i] < self.__heap[bigger_child]:
																							A__						:				List[str]         =			self.__heap[bigger_child]
																							A__						:				Tuple         =			temporary
																	A__						:				Tuple         =			bigger_child







					def 			_lowercase      (							self   :  int):
											A__						:				str         =			self.__heap[1]
											A__						:				Tuple         =			self.__heap[self.__size]
											self.__size -= 1
											self.__heap.pop()
											self.__swap_down(1)
											return max_value







					@property
					def 			_lowercase      (							self   :  Any):
											return self.__heap[1:]







					def __len__(							self   :  Union[str, Any]):
											return self.__size


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		# create an instance of BinaryHeap
		snake_case     :    List[Any]						=    BinaryHeap()
		binary_heap.insert(6)
		binary_heap.insert(1_0)
		binary_heap.insert(1_5)
		binary_heap.insert(1_2)
		# pop root(max-values because it is max heap)
		print(binary_heap.pop())  # 15
		print(binary_heap.pop())  # 12
		# get the list and size after operations
		print(binary_heap.get_list)
		print(len(binary_heap))