



"""simple docstring"""
from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import SwinConfig
from transformers.testing_utils import require_tf, require_vision, slow, to_atuple
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
						import tensorflow as tf

						from transformers.models.swin.modeling_tf_swin import (
						    TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFSwinForImageClassification,
						    TFSwinForMaskedImageModeling,
						    TFSwinModel,
						)


if is_vision_available():
						from PIL import Image

						from transformers import AutoImageProcessor
class 							lowerCamelCase    :






					'''simple docstring'''





					def __init__(			self:				Optional[int]		,	snake_case:				List[Any]		,	snake_case:				List[Any]=13		,	snake_case:				str=32		,	snake_case:				Optional[int]=2		,	snake_case:				int=3		,	snake_case:				Union[str, Any]=16		,	snake_case:				Any=[1, 2, 1]		,	snake_case:				Optional[int]=[2, 2, 4]		,	snake_case:				Tuple=2		,	snake_case:				Any=2.0		,	snake_case:				Dict=True		,	snake_case:				int=0.0		,	snake_case:				Optional[Any]=0.0		,	snake_case:				Any=0.1		,	snake_case:				Optional[Any]="gelu"		,	snake_case:				List[str]=False		,	snake_case:				Tuple=True		,	snake_case:				Tuple=0.0_2		,	snake_case:				List[str]=1E-5		,	snake_case:				Dict=True		,	snake_case:				Optional[int]=None		,	snake_case:				Optional[Any]=True		,	snake_case:				List[Any]=10		,	snake_case:				Optional[int]=8		,	)  ->      None:
											snake_case_					:List[Any]		=     parent
											snake_case_					:Optional[Any]		=     batch_size
											snake_case_					:Any		=     image_size
											snake_case_					:Any		=     patch_size
											snake_case_					:List[str]		=     num_channels
											snake_case_					:Union[str, Any]		=     embed_dim
											snake_case_					:str		=     depths
											snake_case_					:Dict		=     num_heads
											snake_case_					:Optional[int]		=     window_size
											snake_case_					:Dict		=     mlp_ratio
											snake_case_					:Optional[Any]		=     qkv_bias
											snake_case_					:List[Any]		=     hidden_dropout_prob
											snake_case_					:List[str]		=     attention_probs_dropout_prob
											snake_case_					:str		=     drop_path_rate
											snake_case_					:List[Any]		=     hidden_act
											snake_case_					:List[Any]		=     use_absolute_embeddings
											snake_case_					:Dict		=     patch_norm
											snake_case_					:List[Any]		=     layer_norm_eps
											snake_case_					:Optional[int]		=     initializer_range
											snake_case_					:Dict		=     is_training
											snake_case_					:Union[str, Any]		=     scope
											snake_case_					:Dict		=     use_labels
											snake_case_					:str		=     type_sequence_label_size
											snake_case_					:List[Any]		=     encoder_stride






					def   lowerCAmelCase_							(			self:				Optional[int]		)  ->      int:
											snake_case_					:List[str]		=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

											snake_case_					:Optional[Any]		=     None
											if self.use_labels:
																	snake_case_					:Optional[Any]		=     ids_tensor([self.batch_size]		,	self.type_sequence_label_size		)

											snake_case_					:Dict		=     self.get_config()

											return config, pixel_values, labels






					def   lowerCAmelCase_							(			self:				Optional[int]		)  ->      Tuple:
											return SwinConfig(
											    image_size=self.image_size		,	patch_size=self.patch_size		,	num_channels=self.num_channels		,	embed_dim=self.embed_dim		,	depths=self.depths		,	num_heads=self.num_heads		,	window_size=self.window_size		,	mlp_ratio=self.mlp_ratio		,	qkv_bias=self.qkv_bias		,	hidden_dropout_prob=self.hidden_dropout_prob		,	attention_probs_dropout_prob=self.attention_probs_dropout_prob		,	drop_path_rate=self.drop_path_rate		,	hidden_act=self.hidden_act		,	use_absolute_embeddings=self.use_absolute_embeddings		,	path_norm=self.patch_norm		,	layer_norm_eps=self.layer_norm_eps		,	initializer_range=self.initializer_range		,	encoder_stride=self.encoder_stride		,	)






					def   lowerCAmelCase_							(			self:				int		,	snake_case:				Optional[Any]		,	snake_case:				int		,	snake_case:				Optional[int]		)  ->      Optional[int]:
											snake_case_					:int		=     TFSwinModel(config=snake_case		)
											snake_case_					:Optional[int]		=     model(snake_case		)

											snake_case_					:Optional[int]		=     ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths		) - 1))
											snake_case_					:str		=     int(config.embed_dim * 2 ** (len(config.depths		) - 1)		)

											self.parent.assertEqual(result.last_hidden_state.shape		,	(self.batch_size, expected_seq_len, expected_dim)		)






					def   lowerCAmelCase_							(			self:				Optional[int]		,	snake_case:				Union[str, Any]		,	snake_case:				str		,	snake_case:				Union[str, Any]		)  ->      Optional[int]:
											snake_case_					:Optional[Any]		=     TFSwinForMaskedImageModeling(config=snake_case		)
											snake_case_					:Union[str, Any]		=     model(snake_case		)
											self.parent.assertEqual(
											    result.logits.shape		,	(self.batch_size, self.num_channels, self.image_size, self.image_size)		)

											# test greyscale images
											snake_case_					:str		=     1
											snake_case_					:int		=     TFSwinForMaskedImageModeling(snake_case		)

											snake_case_					:List[str]		=     floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
											snake_case_					:Optional[Any]		=     model(snake_case		)
											self.parent.assertEqual(result.logits.shape		,	(self.batch_size, 1, self.image_size, self.image_size)		)






					def   lowerCAmelCase_							(			self:				Optional[Any]		,	snake_case:				int		,	snake_case:				str		,	snake_case:				Optional[int]		)  ->      Optional[int]:
											snake_case_					:Union[str, Any]		=     self.type_sequence_label_size
											snake_case_					:Optional[int]		=     TFSwinForImageClassification(snake_case		)
											snake_case_					:Tuple		=     model(snake_case		,	labels=snake_case		)
											self.parent.assertEqual(result.logits.shape		,	(self.batch_size, self.type_sequence_label_size)		)

											# test greyscale images
											snake_case_					:int		=     1
											snake_case_					:Optional[int]		=     TFSwinForImageClassification(snake_case		)
											snake_case_					:Dict		=     floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
											snake_case_					:Union[str, Any]		=     model(snake_case		)
											self.parent.assertEqual(result.logits.shape		,	(self.batch_size, self.type_sequence_label_size)		)






					def   lowerCAmelCase_							(			self:				List[str]		)  ->      Optional[Any]:
											snake_case_					:Union[str, Any]		=     self.prepare_config_and_inputs()
											snake_case_,			snake_case_,			snake_case_					:List[Any]		=     config_and_inputs
											snake_case_					:str		=     {"""pixel_values""": pixel_values}
											return config, inputs_dict

@require_tf
class 							lowerCamelCase    (     _lowerCAmelCase       ,		_lowerCAmelCase       ,		unittest.TestCase							):






					'''simple docstring'''





					_A						:     Any		=	(
					    (
					        TFSwinModel,
					        TFSwinForImageClassification,
					        TFSwinForMaskedImageModeling,
					    )
					    if is_tf_available()
					    else ()
					)
					_A						:     Optional[int]		=	(
					    {"""feature-extraction""": TFSwinModel, """image-classification""": TFSwinForImageClassification}
					    if is_tf_available()
					    else {}
					)

					_A						:     int		=	False
					_A						:     Optional[Any]		=	False
					_A						:     str		=	False
					_A						:     Any		=	False






					def   lowerCAmelCase_							(			self:				Optional[int]		)  ->      Tuple:
											snake_case_					:int		=     TFSwinModelTester(self		)
											snake_case_					:List[str]		=     ConfigTester(self		,	config_class=snake_case		,	embed_dim=37		)






					def   lowerCAmelCase_							(			self:				Optional[int]		)  ->      Any:
											self.create_and_test_config_common_properties()
											self.config_tester.create_and_test_config_to_json_string()
											self.config_tester.create_and_test_config_to_json_file()
											self.config_tester.create_and_test_config_from_and_save_pretrained()
											self.config_tester.create_and_test_config_with_num_labels()
											self.config_tester.check_config_can_be_init_without_params()
											self.config_tester.check_config_arguments_init()






					def   lowerCAmelCase_							(			self:				List[str]		)  ->      str:
											return






					def   lowerCAmelCase_							(			self:				Dict		)  ->      str:
											snake_case_					:Union[str, Any]		=     self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*snake_case		)






					def   lowerCAmelCase_							(			self:				Any		)  ->      Dict:
											snake_case_					:int		=     self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_masked_image_modeling(*snake_case		)






					def   lowerCAmelCase_							(			self:				List[Any]		)  ->      Union[str, Any]:
											snake_case_					:Tuple		=     self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*snake_case		)






					@unittest.skip(reason="""Swin does not use inputs_embeds"""		)
					def   lowerCAmelCase_							(			self:				List[str]		)  ->      List[Any]:
											pass






					def   lowerCAmelCase_							(			self:				str		)  ->      Tuple:
											snake_case_,			snake_case_					:List[Any]		=     self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																	snake_case_					:str		=     model_class(snake_case		)
																	self.assertIsInstance(model.get_input_embeddings()		,	tf.keras.layers.Layer		)
																	snake_case_					:int		=     model.get_output_embeddings()
																	self.assertTrue(x is None or isinstance(snake_case		,	tf.keras.layers.Dense		)		)






					def   lowerCAmelCase_							(			self:				List[str]		)  ->      Dict:
											snake_case_,			snake_case_					:Optional[Any]		=     self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																	snake_case_					:List[str]		=     model_class(snake_case		)
																	snake_case_					:List[str]		=     inspect.signature(model.call		)
																	# signature.parameters is an OrderedDict => so arg_names order is deterministic
																	snake_case_					:int		=     [*signature.parameters.keys()]

																	snake_case_					:Optional[Any]		=     ["""pixel_values"""]
																	self.assertListEqual(arg_names[:1]		,	snake_case		)






					def   lowerCAmelCase_							(			self:				Union[str, Any]		)  ->      int:
											snake_case_,			snake_case_					:List[str]		=     self.model_tester.prepare_config_and_inputs_for_common()
											snake_case_					:Dict		=     True

											for model_class in self.all_model_classes:
																	snake_case_					:Union[str, Any]		=     True
																	snake_case_					:Union[str, Any]		=     False
																	snake_case_					:int		=     True
																	snake_case_					:int		=     model_class(snake_case		)
																	snake_case_					:Optional[int]		=     model(**self._prepare_for_class(snake_case		,	snake_case		)		)
																	snake_case_					:Dict		=     outputs.attentions
																	snake_case_					:Optional[int]		=     len(self.model_tester.depths		)
																	self.assertEqual(len(snake_case		)		,	snake_case		)

																	# check that output_attentions also work using config
																	del inputs_dict["output_attentions"]
																	snake_case_					:List[str]		=     True
																	snake_case_					:Optional[Any]		=     config.window_size**2
																	snake_case_					:Any		=     model_class(snake_case		)
																	snake_case_					:Optional[Any]		=     model(**self._prepare_for_class(snake_case		,	snake_case		)		)
																	snake_case_					:Optional[int]		=     outputs.attentions
																	self.assertEqual(len(snake_case		)		,	snake_case		)

																	self.assertListEqual(
																	    list(attentions[0].shape[-3:]		)		,	[self.model_tester.num_heads[0], window_size_squared, window_size_squared]		,	)
																	snake_case_					:str		=     len(snake_case		)

																	# Check attention is always last and order is fine
																	snake_case_					:str		=     True
																	snake_case_					:Dict		=     True
																	snake_case_					:Optional[int]		=     model_class(snake_case		)
																	snake_case_					:List[str]		=     model(**self._prepare_for_class(snake_case		,	snake_case		)		)

																	if hasattr(self.model_tester		,	"""num_hidden_states_types"""		):
																							snake_case_					:Any		=     self.model_tester.num_hidden_states_types
																	else:
																							# also another +1 for reshaped_hidden_states
																							snake_case_					:Dict		=     2
																	self.assertEqual(out_len + added_hidden_states		,	len(snake_case		)		)

																	snake_case_					:Optional[int]		=     outputs.attentions

																	self.assertEqual(len(snake_case		)		,	snake_case		)

																	self.assertListEqual(
																	    list(self_attentions[0].shape[-3:]		)		,	[self.model_tester.num_heads[0], window_size_squared, window_size_squared]		,	)






					def   lowerCAmelCase_							(			self:				Optional[int]		,	snake_case:				Optional[int]		,	snake_case:				List[str]		,	snake_case:				Tuple		,	snake_case:				str		)  ->      Any:
											snake_case_					:List[Any]		=     model_class(snake_case		)
											snake_case_					:Any		=     model(**self._prepare_for_class(snake_case		,	snake_case		)		)
											snake_case_					:Optional[int]		=     outputs.hidden_states

											snake_case_					:int		=     getattr(
											    self.model_tester		,	"""expected_num_hidden_layers"""		,	len(self.model_tester.depths		) + 1		)
											self.assertEqual(len(snake_case		)		,	snake_case		)

											# Swin has a different seq_length
											snake_case_					:List[Any]		=     to_atuple(config.patch_size		)

											snake_case_					:Any		=     (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

											self.assertListEqual(
											    list(hidden_states[0].shape[-2:]		)		,	[num_patches, self.model_tester.embed_dim]		,	)

											snake_case_					:Union[str, Any]		=     outputs.reshaped_hidden_states
											self.assertEqual(len(snake_case		)		,	snake_case		)

											snake_case_,			snake_case_,			snake_case_,			snake_case_					:Union[str, Any]		=     reshaped_hidden_states[0].shape

											snake_case_					:Dict		=     tf.reshape(reshaped_hidden_states[0]		,	(batch_size, num_channels, height * width)		)
											snake_case_					:List[str]		=     tf.transpose(snake_case		,	(0, 2, 1)		)

											self.assertListEqual(
											    list(reshaped_hidden_states.shape[-2:]		)		,	[num_patches, self.model_tester.embed_dim]		,	)






					def   lowerCAmelCase_							(			self:				Optional[int]		)  ->      str:
											snake_case_,			snake_case_					:List[Any]		=     self.model_tester.prepare_config_and_inputs_for_common()

											snake_case_					:Optional[Any]		=     to_atuple(self.model_tester.image_size		)

											for model_class in self.all_model_classes:
																	snake_case_					:Dict		=     True
																	self.check_hidden_states_output(snake_case		,	snake_case		,	snake_case		,	snake_case		)

																	# check that output_hidden_states also work using config
																	del inputs_dict["output_hidden_states"]
																	snake_case_					:List[Any]		=     True

																	self.check_hidden_states_output(snake_case		,	snake_case		,	snake_case		,	snake_case		)






					def   lowerCAmelCase_							(			self:				Tuple		)  ->      Tuple:
											snake_case_,			snake_case_					:List[str]		=     self.model_tester.prepare_config_and_inputs_for_common()
											snake_case_					:Tuple		=     3

											snake_case_					:Union[str, Any]		=     to_atuple(self.model_tester.image_size		)
											snake_case_					:List[Any]		=     to_atuple(config.patch_size		)

											snake_case_					:int		=     image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
											snake_case_					:Dict		=     image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

											for model_class in self.all_model_classes:
																	snake_case_					:Any		=     True
																	self.check_hidden_states_output(snake_case		,	snake_case		,	snake_case		,	(padded_height, padded_width)		)

																	# check that output_hidden_states also work using config
																	del inputs_dict["output_hidden_states"]
																	snake_case_					:str		=     True
																	self.check_hidden_states_output(snake_case		,	snake_case		,	snake_case		,	(padded_height, padded_width)		)






					@slow
					def   lowerCAmelCase_							(			self:				Union[str, Any]		)  ->      Optional[int]:
											for model_name in TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	snake_case_					:Optional[Any]		=     TFSwinModel.from_pretrained(snake_case		)
																	self.assertIsNotNone(snake_case		)

@require_vision
@require_tf
class 							lowerCamelCase    (     unittest.TestCase							):






					'''simple docstring'''





					@cached_property
					def   lowerCAmelCase_							(			self:				Optional[Any]		)  ->      Any:
											return (
											    AutoImageProcessor.from_pretrained("""microsoft/swin-tiny-patch4-window7-224"""		)
											    if is_vision_available()
											    else None
											)






					@slow
					def   lowerCAmelCase_							(			self:				List[str]		)  ->      Optional[Any]:
											snake_case_					:str		=     TFSwinForImageClassification.from_pretrained("""microsoft/swin-tiny-patch4-window7-224"""		)
											snake_case_					:List[str]		=     self.default_image_processor

											snake_case_					:List[str]		=     Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""		)
											snake_case_					:List[str]		=     image_processor(images=snake_case		,	return_tensors="""tf"""		)

											# forward pass
											snake_case_					:List[Any]		=     model(snake_case		)

											# verify the logits
											snake_case_					:List[str]		=     tf.TensorShape((1, 1_000)		)
											self.assertEqual(outputs.logits.shape		,	snake_case		)
											snake_case_					:Optional[int]		=     tf.constant([-0.0_9_4_8, -0.6_4_5_4, -0.0_9_2_1]		)
											self.assertTrue(np.allclose(outputs.logits[0, :3]		,	snake_case		,	atol=1E-4		)		)




"""simple docstring"""






class       lowerCamelCase_		:





				"""simple docstring"""







				def __init__(    self  ):
								SCREAMING_SNAKE_CASE__  				=					[0]
								SCREAMING_SNAKE_CASE__  				=					0



				def 	lowerCAmelCase__		(    self							,		UpperCAmelCase__  ):
								SCREAMING_SNAKE_CASE__  				=					self.__heap[i]
								while i // 2 > 0:
												if self.__heap[i] > self.__heap[i // 2]:
																SCREAMING_SNAKE_CASE__  				=					self.__heap[i // 2]
																SCREAMING_SNAKE_CASE__  				=					temporary
												i //= 2



				def 	lowerCAmelCase__		(    self							,		UpperCAmelCase__  ):
								self.__heap.append(UpperCAmelCase__  )
								self.__size += 1
								self.__swap_up(self.__size  )



				def 	lowerCAmelCase__		(    self							,		UpperCAmelCase__  ):
								while self.__size >= 2 * i:
												if 2 * i + 1 > self.__size:
																SCREAMING_SNAKE_CASE__  				=					2 * i
												else:
																if self.__heap[2 * i] > self.__heap[2 * i + 1]:
																				SCREAMING_SNAKE_CASE__  				=					2 * i
																else:
																				SCREAMING_SNAKE_CASE__  				=					2 * i + 1
												SCREAMING_SNAKE_CASE__  				=					self.__heap[i]
												if self.__heap[i] < self.__heap[bigger_child]:
																SCREAMING_SNAKE_CASE__  				=					self.__heap[bigger_child]
																SCREAMING_SNAKE_CASE__  				=					temporary
												SCREAMING_SNAKE_CASE__  				=					bigger_child



				def 	lowerCAmelCase__		(    self  ):
								SCREAMING_SNAKE_CASE__  				=					self.__heap[1]
								SCREAMING_SNAKE_CASE__  				=					self.__heap[self.__size]
								self.__size -= 1
								self.__heap.pop()
								self.__swap_down(1  )
								return max_value



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






				def __len__(    self  ):
								return self.__size


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	# create an instance of BinaryHeap
	_lowerCamelCase  =	BinaryHeap()
	binary_heap.insert(6)
	binary_heap.insert(10)
	binary_heap.insert(15)
	binary_heap.insert(12)
	# 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))
