


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

from collections import namedtuple
from dataclasses import dataclass



@dataclass
class 			_UpperCAmelCase :






		'''simple docstring'''

		lowerCamelCase__				=42
		lowerCamelCase__				=None
		lowerCamelCase__				=None

SCREAMING_SNAKE_CASE :    Any									=				namedtuple("""CoinsDistribResult""", """moves excess""")






def 		lowercase							(     _snake_case						:      TreeNode | None     )     ->int:
	"""simple docstring"""





	if root is None:
		return 0

	# Validation
	def count_nodes(_snake_case						:      TreeNode | None     ) -> int:
		if node is None:
			return 0

		return count_nodes(node.left     ) + count_nodes(node.right     ) + 1

	def count_coins(_snake_case						:      TreeNode | None     ) -> int:
		if node is None:
			return 0

		return count_coins(node.left     ) + count_coins(node.right     ) + node.data

	if count_nodes(_snake_case     ) != count_coins(_snake_case     ):
		raise ValueError('''The nodes number should be same as the number of coins'''     )

	# Main calculation
	def get_distrib(_snake_case						:      TreeNode | None     ) -> CoinsDistribResult:

		if node is None:
			return CoinsDistribResult(0     ,   1     )

		__snake_case	,							__snake_case	:						int						=				get_distrib(node.left     )
		__snake_case	,							__snake_case	:						List[str]						=				get_distrib(node.right     )

		__snake_case	:						Any						=				1 - left_distrib_excess
		__snake_case	:						Tuple						=				1 - right_distrib_excess

		__snake_case	:						Any						=				(
		    left_distrib_moves
		    + right_distrib_moves
		    + abs(_snake_case     )
		    + abs(_snake_case     )
		)
		__snake_case	:						Dict						=				node.data - coins_to_left - coins_to_right

		return CoinsDistribResult(_snake_case     ,   _snake_case     )

	return get_distrib(_snake_case     )[0]


if __name__ == "__main__":
			import doctest

			doctest.testmod()









"""simple docstring"""
import inspect
import unittest

from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers import ConvNextVaBackbone, ConvNextVaForImageClassification, ConvNextVaModel
			from transformers.models.convnextva.modeling_convnextva import CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import AutoImageProcessor



class 			_UpperCAmelCase :






		'''simple docstring'''

		def __init__(self				,       a_				,       a_=13				,       a_=32				,       a_=3				,       a_=4				,       a_=[10, 20, 30, 40]				,       a_=[2, 2, 3, 2]				,       a_=True				,       a_=True				,       a_=37				,       a_="gelu"				,       a_=10				,       a_=0.02				,       a_=["stage2", "stage3", "stage4"]				,       a_=[2, 3, 4]				,       a_=None				,       ):





			'''simple docstring'''

			__snake_case	:						List[str]						=				parent
			__snake_case	:						str						=				batch_size
			__snake_case	:						List[Any]						=				image_size
			__snake_case	:						List[Any]						=				num_channels
			__snake_case	:						str						=				num_stages
			__snake_case	:						Any						=				hidden_sizes
			__snake_case	:						Optional[int]						=				depths
			__snake_case	:						Dict						=				is_training
			__snake_case	:						Tuple						=				use_labels
			__snake_case	:						str						=				intermediate_size
			__snake_case	:						Optional[int]						=				hidden_act
			__snake_case	:						Dict						=				num_labels
			__snake_case	:						Tuple						=				initializer_range
			__snake_case	:						Dict						=				out_features
			__snake_case	:						Optional[int]						=				out_indices
			__snake_case	:						str						=				scope



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Optional[Any]						=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

			__snake_case	:						str						=				None
			if self.use_labels:
				__snake_case	:						Any						=				ids_tensor([self.batch_size]				,       self.num_labels					)

			__snake_case	:						Dict						=				self.get_config()

			return config, pixel_values, labels



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return ConvNextVaConfig(
			    num_channels=self.num_channels				,       hidden_sizes=self.hidden_sizes				,       depths=self.depths				,       num_stages=self.num_stages				,       hidden_act=self.hidden_act				,       is_decoder=a_				,       initializer_range=self.initializer_range				,       out_features=self.out_features				,       out_indices=self.out_indices				,       num_labels=self.num_labels				,       )



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_				,       a_					):





			'''simple docstring'''

			__snake_case	:						Optional[int]						=				ConvNextVaModel(config=a_					)
			model.to(a_					)
			model.eval()
			__snake_case	:						Any						=				model(a_					)
			# expected last hidden states: B, C, H // 32, W // 32
			self.parent.assertEqual(
			    result.last_hidden_state.shape				,       (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)				,       )



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_				,       a_					):





			'''simple docstring'''

			__snake_case	:						Any						=				ConvNextVaForImageClassification(a_					)
			model.to(a_					)
			model.eval()
			__snake_case	:						Union[str, Any]						=				model(a_				,       labels=a_					)
			self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.num_labels)					)



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_				,       a_					):





			'''simple docstring'''

			__snake_case	:						Dict						=				ConvNextVaBackbone(config=a_					)
			model.to(a_					)
			model.eval()
			__snake_case	:						Tuple						=				model(a_					)

			# verify hidden states
			self.parent.assertEqual(len(result.feature_maps					)				,       len(config.out_features					)					)
			self.parent.assertListEqual(list(result.feature_maps[0].shape					)				,       [self.batch_size, self.hidden_sizes[1], 4, 4]					)

			# verify channels
			self.parent.assertEqual(len(model.channels					)				,       len(config.out_features					)					)
			self.parent.assertListEqual(model.channels				,       config.hidden_sizes[1:]					)

			# verify backbone works with out_features=None
			__snake_case	:						str						=				None
			__snake_case	:						Optional[Any]						=				ConvNextVaBackbone(config=a_					)
			model.to(a_					)
			model.eval()
			__snake_case	:						Tuple						=				model(a_					)

			# verify feature maps
			self.parent.assertEqual(len(result.feature_maps					)				,       1					)
			self.parent.assertListEqual(list(result.feature_maps[0].shape					)				,       [self.batch_size, self.hidden_sizes[-1], 1, 1]					)

			# verify channels
			self.parent.assertEqual(len(model.channels					)				,       1					)
			self.parent.assertListEqual(model.channels				,       [config.hidden_sizes[-1]]					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						int						=				self.prepare_config_and_inputs()
			__snake_case	,							__snake_case	,							__snake_case	:						Union[str, Any]						=				config_and_inputs
			__snake_case	:						Any						=				{'''pixel_values''': pixel_values}
			return config, inputs_dict



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						int						=				self.prepare_config_and_inputs()
			__snake_case	,							__snake_case	,							__snake_case	:						Union[str, Any]						=				config_and_inputs
			__snake_case	:						Optional[int]						=				{'''pixel_values''': pixel_values, '''labels''': labels}
			return config, inputs_dict



@require_torch
class 			_UpperCAmelCase (							__snake_case,				__snake_case,				unittest.TestCase				):






		'''simple docstring'''

		lowerCamelCase__				=(
		    (
		        ConvNextVaModel,
		        ConvNextVaForImageClassification,
		        ConvNextVaBackbone,
		    )
		    if is_torch_available()
		    else ()
		)
		lowerCamelCase__				=(
		    {'feature-extraction': ConvNextVaModel, 'image-classification': ConvNextVaForImageClassification}
		    if is_torch_available()
		    else {}
		)

		lowerCamelCase__				=False
		lowerCamelCase__				=False
		lowerCamelCase__				=False
		lowerCamelCase__				=False
		lowerCamelCase__				=False



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Dict						=				ConvNextVaModelTester(self					)
			__snake_case	:						Union[str, Any]						=				ConfigTester(self				,       config_class=a_				,       has_text_modality=a_				,       hidden_size=37					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			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 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return



		@unittest.skip(reason='''ConvNextV2 does not use inputs_embeds'''					)
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			pass



		@unittest.skip(reason='''ConvNextV2 does not support input and output embeddings'''					)
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			pass



		@unittest.skip(reason='''ConvNextV2 does not use feedforward chunking'''					)
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			pass



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			if not self.model_tester.is_training:
				return

			for model_class in self.all_model_classes:
				__snake_case	,							__snake_case	:						str						=				self.model_tester.prepare_config_and_inputs_with_labels()
				__snake_case	:						int						=				True

				if model_class.__name__ in [
				    *get_values(a_					),
				    *get_values(a_					),
				]:
					continue

				__snake_case	:						Dict						=				model_class(a_					)
				model.to(a_					)
				model.train()
				__snake_case	:						Tuple						=				self._prepare_for_class(a_				,       a_				,       return_labels=a_					)
				__snake_case	:						List[str]						=				model(**a_					).loss
				loss.backward()



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			if not self.model_tester.is_training:
				return

			for model_class in self.all_model_classes:
				__snake_case	,							__snake_case	:						Optional[Any]						=				self.model_tester.prepare_config_and_inputs_with_labels()
				__snake_case	:						Optional[Any]						=				False
				__snake_case	:						Tuple						=				True

				if (
				    model_class.__name__
				    in [*get_values(a_					), *get_values(a_					)]
				    or not model_class.supports_gradient_checkpointing
				):
					continue

				__snake_case	:						Union[str, Any]						=				model_class(a_					)
				model.to(a_					)
				model.gradient_checkpointing_enable()
				model.train()
				__snake_case	:						str						=				self._prepare_for_class(a_				,       a_				,       return_labels=a_					)
				__snake_case	:						Union[str, Any]						=				model(**a_					).loss
				loss.backward()



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__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	:						Optional[Any]						=				model_class(a_					)
				__snake_case	:						int						=				inspect.signature(model.forward					)
				# signature.parameters is an OrderedDict => so arg_names order is deterministic
				__snake_case	:						int						=				[*signature.parameters.keys()]

				__snake_case	:						Any						=				['''pixel_values''']
				self.assertListEqual(arg_names[:1]				,       a_					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						str						=				self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*a_					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			def check_hidden_states_output(a_				,       a_				,       a_					):
				__snake_case	:						Tuple						=				model_class(a_					)
				model.to(a_					)
				model.eval()

				with torch.no_grad():
					__snake_case	:						Union[str, Any]						=				model(**self._prepare_for_class(a_				,       a_					)					)

				__snake_case	:						Union[str, Any]						=				outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

				__snake_case	:						List[Any]						=				self.model_tester.num_stages
				self.assertEqual(len(a_					)				,       expected_num_stages + 1					)

				# ConvNextV2's feature maps are of shape (batch_size, num_channels, height, width)
				self.assertListEqual(
				    list(hidden_states[0].shape[-2:]					)				,       [self.model_tester.image_size // 4, self.model_tester.image_size // 4]				,       )

			__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						=				True
				check_hidden_states_output(a_				,       a_				,       a_					)

				# check that output_hidden_states also work using config
				del inputs_dict["output_hidden_states"]
				__snake_case	:						str						=				True

				check_hidden_states_output(a_				,       a_				,       a_					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						str						=				self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_image_classification(*a_					)



		@slow
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
				__snake_case	:						str						=				ConvNextVaModel.from_pretrained(a_					)
				self.assertIsNotNone(a_					)






def 		lowercase							(     )     ->Any:
	"""simple docstring"""





	__snake_case	:						List[str]						=				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''     )
	return image



@require_torch
@require_vision
class 			_UpperCAmelCase (							unittest.TestCase				):






		'''simple docstring'''

		@cached_property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return AutoImageProcessor.from_pretrained('''facebook/convnextv2-tiny-1k-224'''					) if is_vision_available() else None



		@slow
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						str						=				ConvNextVaForImageClassification.from_pretrained('''facebook/convnextv2-tiny-1k-224'''					).to(a_					)

			__snake_case	:						Dict						=				self.default_image_processor
			__snake_case	:						Tuple						=				prepare_img()
			__snake_case	:						Tuple						=				preprocessor(images=a_				,       return_tensors='''pt'''					).to(a_					)

			# forward pass
			with torch.no_grad():
				__snake_case	:						Union[str, Any]						=				model(**a_					)

			# verify the logits
			__snake_case	:						Tuple						=				torch.Size((1, 10_00)					)
			self.assertEqual(outputs.logits.shape				,       a_					)

			__snake_case	:						Any						=				torch.tensor([0.9996, 0.1966, -0.4386]					).to(a_					)
			self.assertTrue(torch.allclose(outputs.logits[0, :3]				,       a_				,       atol=1E-4					)					)





