




'''simple docstring'''





import inspect
import unittest

from transformers import MobileNetVaConfig
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 MobileNetVaForImageClassification, MobileNetVaModel
			from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import MobileNetVaImageProcessor

class        _a    (						__lowerCAmelCase							):






					def     _lowercase     (			self   )							->      Optional[Any]:
										_snake_case							=				self.config_class(**self.inputs_dict   )
										self.parent.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,"tf_padding"   )   )
										self.parent.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,"depth_multiplier"   )   )







class        _a    :






					def __init__(			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=13 ,_SCREAMING_SNAKE_CASE=3 ,_SCREAMING_SNAKE_CASE=32 ,_SCREAMING_SNAKE_CASE=0.2_5 ,_SCREAMING_SNAKE_CASE=8 ,_SCREAMING_SNAKE_CASE=True ,_SCREAMING_SNAKE_CASE=1_024 ,_SCREAMING_SNAKE_CASE=32 ,_SCREAMING_SNAKE_CASE="relu6" ,_SCREAMING_SNAKE_CASE=0.1 ,_SCREAMING_SNAKE_CASE=0.0_2 ,_SCREAMING_SNAKE_CASE=True ,_SCREAMING_SNAKE_CASE=True ,_SCREAMING_SNAKE_CASE=10 ,_SCREAMING_SNAKE_CASE=None ,)							->      List[Any]:
										_snake_case							=				parent
										_snake_case							=				batch_size
										_snake_case							=				num_channels
										_snake_case							=				image_size
										_snake_case							=				depth_multiplier
										_snake_case							=				min_depth
										_snake_case							=				tf_padding
										_snake_case							=				int(last_hidden_size * depth_multiplier   )
										_snake_case							=				output_stride
										_snake_case							=				hidden_act
										_snake_case							=				classifier_dropout_prob
										_snake_case							=				use_labels
										_snake_case							=				is_training
										_snake_case							=				num_labels
										_snake_case							=				initializer_range
										_snake_case							=				scope






					def     _lowercase     (			self   )							->      Optional[Any]:
										_snake_case							=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

										_snake_case							=				None
										_snake_case							=				None
										if self.use_labels:
															_snake_case							=				ids_tensor([self.batch_size] ,self.num_labels   )
															_snake_case							=				ids_tensor([self.batch_size, self.image_size, self.image_size] ,self.num_labels   )

										_snake_case							=				self.get_config()

										return config, pixel_values, labels, pixel_labels






					def     _lowercase     (			self   )							->      Union[str, Any]:
										return MobileNetVaConfig(
										    num_channels=self.num_channels ,image_size=self.image_size ,depth_multiplier=self.depth_multiplier ,min_depth=self.min_depth ,tf_padding=self.tf_padding ,hidden_act=self.hidden_act ,classifier_dropout_prob=self.classifier_dropout_prob ,initializer_range=self.initializer_range ,)






					def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )							->      int:
										_snake_case							=				MobileNetVaModel(config=_SCREAMING_SNAKE_CASE   )
										model.to(_SCREAMING_SNAKE_CASE   )
										model.eval()
										_snake_case							=				model(_SCREAMING_SNAKE_CASE   )
										self.parent.assertEqual(
										    result.last_hidden_state.shape ,(
										        self.batch_size,
										        self.last_hidden_size,
										        self.image_size // self.output_stride,
										        self.image_size // self.output_stride,
										    ) ,)






					def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )							->      Tuple:
										_snake_case							=				self.num_labels
										_snake_case							=				MobileNetVaForImageClassification(_SCREAMING_SNAKE_CASE   )
										model.to(_SCREAMING_SNAKE_CASE   )
										model.eval()
										_snake_case							=				model(_SCREAMING_SNAKE_CASE ,labels=_SCREAMING_SNAKE_CASE   )
										self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_labels)   )






					def     _lowercase     (			self   )							->      Optional[int]:
										_snake_case							=				self.prepare_config_and_inputs()
										_snake_case							, _snake_case							, _snake_case							, _snake_case							=				config_and_inputs
										_snake_case							=				{"pixel_values": pixel_values}
										return config, inputs_dict







@require_torch
class        _a    (						__lowerCAmelCase , __lowerCAmelCase , unittest.TestCase							):
					SCREAMING_SNAKE_CASE_							:   Any       =						(MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
					SCREAMING_SNAKE_CASE_							:   List[Any]       =						(
					    {"""feature-extraction""": MobileNetVaModel, """image-classification""": MobileNetVaForImageClassification}
					    if is_torch_available()
					    else {}
					)

					SCREAMING_SNAKE_CASE_							:   str       =						False
					SCREAMING_SNAKE_CASE_							:   List[Any]       =						False
					SCREAMING_SNAKE_CASE_							:   Tuple       =						False
					SCREAMING_SNAKE_CASE_							:   Union[str, Any]       =						False






					def     _lowercase     (			self   )							->      Dict:
										_snake_case							=				MobileNetVaModelTester(self   )
										_snake_case							=				MobileNetVaConfigTester(self ,config_class=_SCREAMING_SNAKE_CASE ,has_text_modality=_SCREAMING_SNAKE_CASE   )






					def     _lowercase     (			self   )							->      List[Any]:
										self.config_tester.run_common_tests()






					@unittest.skip(reason="MobileNetV1 does not use inputs_embeds"   )
					def     _lowercase     (			self   )							->      Optional[int]:
										pass






					@unittest.skip(reason="MobileNetV1 does not support input and output embeddings"   )
					def     _lowercase     (			self   )							->      str:
										pass






					@unittest.skip(reason="MobileNetV1 does not output attentions"   )
					def     _lowercase     (			self   )							->      Dict:
										pass






					def     _lowercase     (			self   )							->      str:
										_snake_case							, _snake_case							=				self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
															_snake_case							=				model_class(_SCREAMING_SNAKE_CASE   )
															_snake_case							=				inspect.signature(model.forward   )
															# signature.parameters is an OrderedDict => so arg_names order is deterministic
															_snake_case							=				[*signature.parameters.keys()]

															_snake_case							=				["pixel_values"]
															self.assertListEqual(arg_names[:1] ,_SCREAMING_SNAKE_CASE   )






					def     _lowercase     (			self   )							->      Optional[int]:
										_snake_case							=				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE   )






					def     _lowercase     (			self   )							->      List[str]:
										def check_hidden_states_output(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   ):
															_snake_case							=				model_class(_SCREAMING_SNAKE_CASE   )
															model.to(_SCREAMING_SNAKE_CASE   )
															model.eval()

															with torch.no_grad():
																				_snake_case							=				model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )   )

															_snake_case							=				outputs.hidden_states

															_snake_case							=				26
															self.assertEqual(len(_SCREAMING_SNAKE_CASE   ) ,_SCREAMING_SNAKE_CASE   )

										_snake_case							, _snake_case							=				self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
															_snake_case							=				True
															check_hidden_states_output(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )

															# check that output_hidden_states also work using config
															del inputs_dict["output_hidden_states"]
															_snake_case							=				True

															check_hidden_states_output(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )






					def     _lowercase     (			self   )							->      List[Any]:
										_snake_case							=				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE   )






					@slow
					def     _lowercase     (			self   )							->      Optional[Any]:
										for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															_snake_case							=				MobileNetVaModel.from_pretrained(_SCREAMING_SNAKE_CASE   )
															self.assertIsNotNone(_SCREAMING_SNAKE_CASE   )







def      __a     (       )						->			Optional[int]:

					"""simple docstring"""




					_snake_case							=				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"   )
					return image





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






					@cached_property
					def     _lowercase     (			self   )							->      Tuple:
										return (
										    MobileNetVaImageProcessor.from_pretrained("google/mobilenet_v1_1.0_224"   ) if is_vision_available() else None
										)






					@slow
					def     _lowercase     (			self   )							->      Any:
										_snake_case							=				MobileNetVaForImageClassification.from_pretrained("google/mobilenet_v1_1.0_224"   ).to(_SCREAMING_SNAKE_CASE   )

										_snake_case							=				self.default_image_processor
										_snake_case							=				prepare_img()
										_snake_case							=				image_processor(images=_SCREAMING_SNAKE_CASE ,return_tensors="pt"   ).to(_SCREAMING_SNAKE_CASE   )

										# forward pass
										with torch.no_grad():
															_snake_case							=				model(**_SCREAMING_SNAKE_CASE   )

										# verify the logits
										_snake_case							=				torch.Size((1, 1_001)   )
										self.assertEqual(outputs.logits.shape ,_SCREAMING_SNAKE_CASE   )

										_snake_case							=				torch.tensor([-4.1_7_3_9, -1.1_2_3_3, 3.1_2_0_5]   ).to(_SCREAMING_SNAKE_CASE   )

										self.assertTrue(torch.allclose(outputs.logits[0, :3] ,_SCREAMING_SNAKE_CASE ,atol=1e-4   )   )








'''simple docstring'''





from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass

@dataclass
class        _a    :
     SCREAMING_SNAKE_CASE_							:   int
     SCREAMING_SNAKE_CASE_							:   TreeNode | None       =						None
     SCREAMING_SNAKE_CASE_							:   TreeNode | None       =						None







UpperCamelCase_						:      Any       =						namedtuple('''CoinsDistribResult''', '''moves excess''')





def      __a     (       _UpperCamelCase:   TreeNode | None   )						->			int:

     """simple docstring"""




     if root is None:
          return 0

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

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

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

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

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

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

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

          _snake_case							, _snake_case							=				get_distrib(node.left   )
          _snake_case							, _snake_case							=				get_distrib(node.right   )

          _snake_case							=				1 - left_distrib_excess
          _snake_case							=				1 - right_distrib_excess

          _snake_case							=				(
              left_distrib_moves
              + right_distrib_moves
              + abs(_UpperCamelCase   )
              + abs(_UpperCamelCase   )
          )
          _snake_case							=				node.data - coins_to_left - coins_to_right

          return CoinsDistribResult(_UpperCamelCase ,						_UpperCamelCase   )

     return get_distrib(_UpperCamelCase   )[0]


if __name__ == "__main__":
   import doctest

   doctest.testmod()


