





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     snake_case     (			self		:   Optional[Any]							)     ->				Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase      : List[str]         =   self.config_class(**self.inputs_dict							)
										self.parent.assertTrue(hasattr(lowerCAmelCase__,			'''tf_padding'''							)							)
										self.parent.assertTrue(hasattr(lowerCAmelCase__,			'''depth_multiplier'''							)							)




class 			_a						:






				def __init__(			self		:   Any,			lowerCAmelCase__		:   Union[str, Any],			lowerCAmelCase__		:   Optional[Any]=1_3,			lowerCAmelCase__		:   Dict=3,			lowerCAmelCase__		:   Optional[Any]=3_2,			lowerCAmelCase__		:   Dict=0.25,			lowerCAmelCase__		:   int=8,			lowerCAmelCase__		:   Optional[int]=True,			lowerCAmelCase__		:   Dict=1_0_2_4,			lowerCAmelCase__		:   int=3_2,			lowerCAmelCase__		:   Optional[int]="relu6",			lowerCAmelCase__		:   Dict=0.1,			lowerCAmelCase__		:   Optional[int]=0.02,			lowerCAmelCase__		:   str=True,			lowerCAmelCase__		:   List[str]=True,			lowerCAmelCase__		:   List[str]=1_0,			lowerCAmelCase__		:   Union[str, Any]=None,			)     ->				Dict:




										'''simple docstring'''



										_UpperCamelCase      : List[Any]         =   parent
										_UpperCamelCase      : Any         =   batch_size
										_UpperCamelCase      : List[str]         =   num_channels
										_UpperCamelCase      : Tuple         =   image_size
										_UpperCamelCase      : Tuple         =   depth_multiplier
										_UpperCamelCase      : Optional[Any]         =   min_depth
										_UpperCamelCase      : int         =   tf_padding
										_UpperCamelCase      : Tuple         =   int(last_hidden_size * depth_multiplier							)
										_UpperCamelCase      : Optional[int]         =   output_stride
										_UpperCamelCase      : int         =   hidden_act
										_UpperCamelCase      : str         =   classifier_dropout_prob
										_UpperCamelCase      : Dict         =   use_labels
										_UpperCamelCase      : Any         =   is_training
										_UpperCamelCase      : List[str]         =   num_labels
										_UpperCamelCase      : Dict         =   initializer_range
										_UpperCamelCase      : Union[str, Any]         =   scope






				def     snake_case     (			self		:   List[Any]							)     ->				Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase      : Union[str, Any]         =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

										_UpperCamelCase      : Dict         =   None
										_UpperCamelCase      : List[Any]         =   None
										if self.use_labels:
																_UpperCamelCase      : Union[str, Any]         =   ids_tensor([self.batch_size],			self.num_labels							)
																_UpperCamelCase      : Union[str, Any]         =   ids_tensor([self.batch_size, self.image_size, self.image_size],			self.num_labels							)

										_UpperCamelCase      : Any         =   self.get_config()

										return config, pixel_values, labels, pixel_labels






				def     snake_case     (			self		:   List[Any]							)     ->				Optional[Any]:




										'''simple docstring'''



										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     snake_case     (			self		:   str,			lowerCAmelCase__		:   Tuple,			lowerCAmelCase__		:   List[Any],			lowerCAmelCase__		:   str,			lowerCAmelCase__		:   Tuple							)     ->				Dict:




										'''simple docstring'''



										_UpperCamelCase      : Optional[Any]         =   MobileNetVaModel(config=lowerCAmelCase__							)
										model.to(lowerCAmelCase__							)
										model.eval()
										_UpperCamelCase      : str         =   model(lowerCAmelCase__							)
										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     snake_case     (			self		:   Dict,			lowerCAmelCase__		:   str,			lowerCAmelCase__		:   List[str],			lowerCAmelCase__		:   Any,			lowerCAmelCase__		:   List[Any]							)     ->				List[Any]:




										'''simple docstring'''



										_UpperCamelCase      : str         =   self.num_labels
										_UpperCamelCase      : Tuple         =   MobileNetVaForImageClassification(lowerCAmelCase__							)
										model.to(lowerCAmelCase__							)
										model.eval()
										_UpperCamelCase      : str         =   model(lowerCAmelCase__,			labels=lowerCAmelCase__							)
										self.parent.assertEqual(result.logits.shape,			(self.batch_size, self.num_labels)							)






				def     snake_case     (			self		:   Optional[Any]							)     ->				str:




										'''simple docstring'''



										_UpperCamelCase      : int         =   self.prepare_config_and_inputs()
										_UpperCamelCase      : List[str]         =   config_and_inputs
										_UpperCamelCase      : Tuple         =   {'''pixel_values''': pixel_values}
										return config, inputs_dict




@require_torch
class 			_a						(				_lowerCAmelCase     ,    _lowerCAmelCase     ,    unittest.TestCase    ):
				UpperCamelCase								=   (MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
				UpperCamelCase								=   (
				    {'''feature-extraction''': MobileNetVaModel, '''image-classification''': MobileNetVaForImageClassification}
				    if is_torch_available()
				    else {}
				)

				UpperCamelCase								=   False
				UpperCamelCase								=   False
				UpperCamelCase								=   False
				UpperCamelCase								=   False






				def     snake_case     (			self		:   Any							)     ->				Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase      : Dict         =   MobileNetVaModelTester(self							)
										_UpperCamelCase      : str         =   MobileNetVaConfigTester(self,			config_class=lowerCAmelCase__,			has_text_modality=lowerCAmelCase__							)






				def     snake_case     (			self		:   Optional[Any]							)     ->				Any:




										'''simple docstring'''



										self.config_tester.run_common_tests()






				@unittest.skip(reason='''MobileNetV1 does not use inputs_embeds'''							)
				def     snake_case     (			self		:   int							)     ->				List[str]:




										'''simple docstring'''



										pass






				@unittest.skip(reason='''MobileNetV1 does not support input and output embeddings'''							)
				def     snake_case     (			self		:   str							)     ->				Dict:




										'''simple docstring'''



										pass






				@unittest.skip(reason='''MobileNetV1 does not output attentions'''							)
				def     snake_case     (			self		:   Union[str, Any]							)     ->				Dict:




										'''simple docstring'''



										pass






				def     snake_case     (			self		:   Union[str, Any]							)     ->				List[Any]:




										'''simple docstring'''



										_UpperCamelCase      : List[Any]         =   self.model_tester.prepare_config_and_inputs_for_common()

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

																_UpperCamelCase      : int         =   ['''pixel_values''']
																self.assertListEqual(arg_names[:1],			lowerCAmelCase__							)






				def     snake_case     (			self		:   int							)     ->				Tuple:




										'''simple docstring'''



										_UpperCamelCase      : Optional[Any]         =   self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCAmelCase__							)






				def     snake_case     (			self		:   Union[str, Any]							)     ->				Optional[int]:




										'''simple docstring'''



										def check_hidden_states_output(lowerCAmelCase__		:   Any,			lowerCAmelCase__		:   str,			lowerCAmelCase__		:   List[Any]							):
																_UpperCamelCase      : Union[str, Any]         =   model_class(lowerCAmelCase__							)
																model.to(lowerCAmelCase__							)
																model.eval()

																with torch.no_grad():
																						_UpperCamelCase      : Any         =   model(**self._prepare_for_class(lowerCAmelCase__,			lowerCAmelCase__							)							)

																_UpperCamelCase      : Optional[int]         =   outputs.hidden_states

																_UpperCamelCase      : Tuple         =   2_6
																self.assertEqual(len(lowerCAmelCase__							),			lowerCAmelCase__							)

										_UpperCamelCase      : List[str]         =   self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																_UpperCamelCase      : Optional[int]         =   True
																check_hidden_states_output(lowerCAmelCase__,			lowerCAmelCase__,			lowerCAmelCase__							)

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

																check_hidden_states_output(lowerCAmelCase__,			lowerCAmelCase__,			lowerCAmelCase__							)






				def     snake_case     (			self		:   Optional[Any]							)     ->				List[str]:




										'''simple docstring'''



										_UpperCamelCase      : int         =   self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__							)






				@slow
				def     snake_case     (			self		:   Dict							)     ->				List[str]:




										'''simple docstring'''



										for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																_UpperCamelCase      : Union[str, Any]         =   MobileNetVaModel.from_pretrained(lowerCAmelCase__							)
																self.assertIsNotNone(lowerCAmelCase__							)





def 	a_    (							):
						_UpperCamelCase      : Any         =   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
						return image






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






				@cached_property
				def     snake_case     (			self		:   Dict							)     ->				Optional[Any]:




										'''simple docstring'''



										return (
										    MobileNetVaImageProcessor.from_pretrained('''google/mobilenet_v1_1.0_224'''							) if is_vision_available() else None
										)






				@slow
				def     snake_case     (			self		:   Optional[int]							)     ->				Dict:




										'''simple docstring'''



										_UpperCamelCase      : List[Any]         =   MobileNetVaForImageClassification.from_pretrained('''google/mobilenet_v1_1.0_224'''							).to(lowerCAmelCase__							)

										_UpperCamelCase      : Any         =   self.default_image_processor
										_UpperCamelCase      : Optional[Any]         =   prepare_img()
										_UpperCamelCase      : Dict         =   image_processor(images=lowerCAmelCase__,			return_tensors='''pt'''							).to(lowerCAmelCase__							)

										# forward pass
										with torch.no_grad():
																_UpperCamelCase      : str         =   model(**lowerCAmelCase__							)

										# verify the logits
										_UpperCamelCase      : Dict         =   torch.Size((1, 1_0_0_1)							)
										self.assertEqual(outputs.logits.shape,			lowerCAmelCase__							)

										_UpperCamelCase      : int         =   torch.tensor([-4.1_739, -1.1_233, 3.1_205]							).to(lowerCAmelCase__							)

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










"""simple docstring"""




from __future__ import annotations

import math





def 	a_    (							_lowercase	,						_lowercase	,						_lowercase	,						_lowercase	,						_lowercase       ):

						if depth < 0:
												raise ValueError('''Depth cannot be less than 0'''       )

						if len(_lowercase       ) == 0:
												raise ValueError('''Scores cannot be empty'''       )

						if depth == height:
												return scores[node_index]

						if is_max:
												return max(
												    minimax(depth + 1	,						node_index * 2	,						_lowercase	,						_lowercase	,						_lowercase       )	,						minimax(depth + 1	,						node_index * 2 + 1	,						_lowercase	,						_lowercase	,						_lowercase       )	,						)

						return min(
						    minimax(depth + 1	,						node_index * 2	,						_lowercase	,						_lowercase	,						_lowercase       )	,						minimax(depth + 1	,						node_index * 2 + 1	,						_lowercase	,						_lowercase	,						_lowercase       )	,						)





def 	a_    (							):
						_UpperCamelCase      : Dict         =   [90, 23, 6, 33, 21, 65, 123, 3_4423]
						_UpperCamelCase      : int         =   math.log(len(_lowercase       )	,						2       )
						print('''Optimal value : '''	,						end=''''''       )
						print(minimax(0	,						0	,						_lowercase	,						_lowercase	,						_lowercase       )       )


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						main()



